EVALUATION OF SWITCHGRASS, M. X GIGANTEUS, AND SORGHUM AS BIOMASS 

CROPS: EFFECTS OF ENVIRONMENT AND FIELD MANAGEMENT PRACTICES 

BY

MATTHEW W. MAUGHAN 

DISSERTATION 

Submitted in partial fulfillment of the requirements 

for the degree of Doctor of Philosophy in Crop Sciences 

in the Graduate College of the 

University of Illinois at Urbana-Champaign, 2011 

Urbana, Illinois 

Doctoral Committee: 

 Professor Germán A. Bollero, Chair 

 Assistant Professor DoKyoung Lee, Director of Research 

 Associate Professor Thomas B. Voigt 

 Professor Donald G. Bullock 

 Assistant Professor Fernando E. Miguez, Iowa State University 
ii

 ABSTRACT 

Switchgrass (Panicum virgatum L.), Miscanthus x giganteus (M. x giganteus), and 

sorghum (Sorghum bicolor L.) have been proposed as potential bioenergy feedstock crops. This 

study evaluates how these crops performs in different environments under different crop 

management practices, particularly nitrogen (N) fertilizer rates. Chapter 1 provides the rationale 

of this research and a general discussion of the unique characteristics of these three crops. In 

Chapter 2, an extensive database of switchgrass biomass yields from 106 sites and 45 field 

studies in eastern two thirds of the USA and southeastern Canada is evaluated using descriptive 

statistics, and using a random coefficients model. Switchgrass has been researched extensively 

in North America as a biomass crop and data reported since the 1990’s reveal large variability in 

dry biomass yields which are related to multiple environment and field management practices. 

This analysis describes switchgrass biomass N response, and shows that in addition to N 

fertilizer rate the most important factors affecting switchgrass dry biomass yields are growing 

region, spring precipitation, growing season, ecotype, and harvest timing. Chapter 3 remarks 

that studies reporting M. x giganteus dry biomass yields to date in the USA are few in number 

and little information is available to suggest a suitable growing region. This study investigates 

M. x giganteus in four Midwest and Atlantic Coast environments under three N rates. 

Establishment success, plant growth, morphology, and dry biomass yields were evaluated and 

results reveal no response to N rate during the establishment years, large biomass yield 

differences among environments, and decreased yield when the crop experienced a combination 

of high heat and dry conditions. Chapter 4 introduces two types of sorghum, forage sorghum and 

biomass sorghum (referred to as energy sorghum) which have been proposed as crops with high

biomass production potential although prior to this study no research had evaluated these 
iii

sorghum types grown for biomass in IL. This field study evaluated two forage sorghum and two 

energy sorghum hybrids in four IL environments under different N rates. Measurements of 

morphology and crop growth were measured throughout the growing season, and dry biomass 

yields revealed significant differences between the two sorghum types. The energy sorghum 

hybrids achieved the greatest biomass yields in each environment with the effects of 

environment and N rate affecting the biomass yields. The results of these studies provide 

valuable information for stakeholders, producers, and scientists regarding the impact of 

environment and management practices on biomass yields of switchgrass, M. x giganteus, and 

sorghum. It is necessary that these factors be evaluated prior to making decisions as to which 

crop species and which cultivar or hybrid to plant in a given location. In most cases, no regional 

recommendations for species selection and N fertility rates are adequate and most field 

management practices must be made on a site-by-site basis. 
iv

ACKNOWLEDGMENTS

It has been a pleasure and honor being a graduate student in the Dept. of Crop Sciences at 

the University of Illinois Urbana-Champaign. My time as a graduate student has been a time of 

growth and learning. The knowledge and experience I have gained are invaluable stepping 

stones in my life. 

First I would like to thank my wife, Julie, for her continued sacrifice and support in my 

behalf. Her encouragement and confidence in me have been a source of strength and joy to me, 

and I could not have come this far without her love, companionship, and friendship. She has 

tirelessly and lovingly cared for our four children Timothy, Porter, Emmett, and Amelia, who 

have also been a wonderful support to me, helping me to maintain a balance in life. 

I express many thanks to my dissertation committee members for their support and 

guidance. I am grateful for the opportunity I had to meet Dr. Germán Bollero at an Agronomy 

Society of America meeting several years ago in Salt Lake City when I was applying to graduate 

school. I was fortunate to have been offered an assistantship and co-advised by him as a 

Master’s student and then later offered a position by him and Dr. Tom Voigt to stay at Illinois to 

pursue a Doctoral degree. His guidance as an advisor has been tremendous, and his sincerity and 

concern for me as a person and as a student have truly shaped me into a better and more qualified 

person and agronomist. Serving as a teaching assistant for his course, Crop Science 440, 

Applied Statistical Methods I, truly laid a statistical foundation for me that has been invaluable in 

my research and evaluation of experiments. I thank Dr. DoKyoung (D.K.) Lee for the 

opportunity to be involved in multiple research projects that have taught me many things about 

applied agronomy research and how to set up field experiments so that planting, management, 

and harvesting of the crops are straight forward. Additionally, his knowledge and passion of 
v

biomass crops has been a tremendous asset when considering each of my experiments. We have 

had had many fruitful conversations about field experiments, challenges, and future 

opportunities. I express gratitude for the opportunity of working with Dr. Tom Voigt. Through 

him I was fortunate to be the recipient of a PhD research assistantship funded by the Sun 

Grant/U.S. Department of Energy Regional Biomass Feedstock Partnership. We have had some

enjoyable trips together traveling to field sites in IN, NE, SD, and KY. Our conversations about 

research and life have been helpful in my work. I also thank him for the many editing versions 

of manuscripts that he has provided to help improve my writing and flow of the manuscripts. I 

am thankful to Dr. Fernando Miguez for his invaluable modeling skills, statistical skills, and R 

software skills. His help with statistical modeling and abilities to use R as a statistical modeling 

and graphical tool have been invaluable in my research. He provided a great deal of R code and 

example R code that have given me the opportunity to become experienced with two statistical 

software’s, R and SAS. His published research has also been a valuable resource in my research 

and writing. One of the greatest opportunities I have had as a graduate student is being a 

teaching assistant for Dr. Don Bullock. He gave me the opportunity to be a teaching assistant for 

two courses, Crop Science 542, Applied Statistical Methods II, and Crop Science 541, 

Regression Analysis, which was very challenging and a huge learning experience. This gave me 

the opportunity to be mentored by him, and to work with excellent teaching assistants and 

students. These experiences not only helped me to learn the statistical material very well, but 

also helped become more comfortable teaching and presenting to audiences. The statistical skills 

and resources I gained from these courses have been invaluable in my research, which I have 

used over and over. Because of Dr. Bullock’s passion for analyzing data in the most appropriate 

and correct way, I often evaluated my own work to make sure that it would measure up.
vi

Last but not least I thank the many collaborators, field crews, and undergraduate students 

that have made these experiments possible. Allen Parrish, in particular, was very helpful in 

setting up experiments and harvesting of biomass. 
vii

TABLE OF CONTENTS

CHAPTER 1 Rationale for Research..............................................................1

CHAPTER 2 Management and Environmental Factors that Affect Switchgrass Biomass 

Production...........................................................................9

CHAPTER 3 Miscanthus x giganteus Productivity: The Effects of Management in 

Different Environments...........................................................52

CHAPTER 4 Management of Forage and Energy Sorghum for Biomass Production in 

Illinois...............................................................................86

CHAPTER 5 Supplementary Information to Ch. 4...........................................115

CONCLUDING REMARKS.............................................................................127

APPENDIX A Selected SAS and R code for Ch. 2..............................................132

APPENDIX B Selected SAS and R code for Ch. 3............................................143

APPENDIX C Selected SAS and R code for Ch. 4.............................................156

APPENDIX D Selected SAS code for Ch. 5.....................................................170

CURRICULUM VITAE....................................................................................183
1

CHAPTER 1

Rationale for Research

The need to produce high yielding biomass crops has become an increasingly important 

priority in the USA because of finite energy supplies, increasing energy prices, and volatile 

foreign oil and gas markets (Perlack et al., 2005; Asif and Muneer, 2007; U.S. Chamber of 

Commerce, 2010). The USA accounts for 25% of the world’s oil consumption, primarily to fuel

the transportation and shipping sectors of its economy (Biomass Research and Development 

Board, 2008). These reasons have urged the USA to attempt shifting its reliance on oil and gas 

to more renewable sources of energy and liquid transportation fuel, especially biomass based 

energy (Heaton et al., 2008a). Continued research efforts and the scaling up of a plant and 

biomass based energy industry is necessary to meet the USA’s vision of replacing 30% of 

current petroleum consumption by 2030 with biofuels (Perlack et al., 2005). These efforts will 

also help to meet the government Renewable Fuels Standard (RFS) mandates of producing 136

billion liters of biofuels by the year 2022 (Energy Independence and Security Act, 2007). Of this 

136 billion liters, 79 must be from advanced biofuels (not corn starch) including 60 must come 

from cellulosic feedstock sources. In response, several herbaceous crops are being researched as 

potential sources of bioenergy feedstock that can be utilized as energy sources. Three of these 

crops are switchgrass (Panicum virgatum L.), Miscanthus x giganteus (M. x giganteus) and 

sorghum (Sorghum bicolor L.), and each has been proposed as potential bioenergy crops for the 

USA. These crops provide an opportunity to grow and utilize a renewable source of energy, and 

can help to meet the mandate. Biomass yields and biomass yield potential of these crops has 

been reported but varies greatly between and within them. Since the early 1990’s, switchgrass 

has been researched extensively in North America as a biomass feedstock crop (Wright and 
2

Turhollow, 2010; Wullschleger et al., 2010), while less research has evaluated M. x giganteus

and sorghum as biomass feedstock crops in North America. This dissertation research (Ch.2, Ch. 

3, Ch. 4, and Ch. 5) focuses on the growth, management, dry biomass yield, and statistical 

modeling of these crops. The main objective of this research is to evaluate reported biomass 

yields of switchgrass in North America, describe the factors that most influence these yields, and 

implement field studies to evaluate M. x giganteus and sorghum in different environments, and 

evaluate field management practices, in particular N rate, that influence biomass yield in these 

environments. 

No direct field comparisons among these three crops are made in the research contained 

in Chapters 2, 3, and 4, although this chapter describes some differences between the crops that 

may favor one over the other in certain situations or environments. Particular emphasis is placed 

on the Midwest, although the area that is covered by each research project is different. Chapter 2 

covers the largest area investigating switchgrass biomass yields across the eastern two thirds of 

the U.S. and southern Canada. Chapter 3 evaluates M. x giganteus in three Midwest 

environments and one Atlantic Coast environment. Finally, Chapter 4 is confined to evaluating 

sorghum in four IL environments. Literature review for each of these crops and their specific 

research objectives are primarily contained in their respective chapters. Commonalities that are 

focused on in each chapter relate to the biomass yield and N response of the crops. Nitrogen 

response in these crops has varied significantly from site-to-site as described in Chapter’s 2, 3, 

and 4, while biomass yield is arguably the most important characteristic to consider when 

selecting a biomass crop. Additionally, these crops are all C4 photosynthetic grasses and as such 

exhibit higher photosynthetic carbon uptake rates than C3 crops (Hopkins and Hüner, 2004). 

When considering the reaching effects of a biomass based energy industry it is necessary to take 
3

a skeptical or investigative approach when evaluating these crops. This is necessary to fully 

understand the impacts that such production could have on current cropping systems, and the 

difficulty of incorporating such crops into current cropping systems, in particular Midwest 

cropping systems.

Large scale bioenergy production of M. x giganteus, switchgrass, and sorghum in the 

Midwest, may or may not be adopted by farmers in the future given the dominance of corn (Zea 

mays L.) and soybean [Glycine max (L.) Merr.] production. Also, if market prices of corn, 

soybean, and wheat (Triticum aestivum L.) remain high as they have during 2010 and 2011, 

much leverage would be required to convince farmers to grow bioenergy crops that have an 

uncertain market and future. One of the purposes of this research is to develop useful agronomic 

information for stakeholders, producers, and researchers that will help them make this decision. 

Currently, little infrastructure or industry in the Midwest exists for processing and handling 

dedicated biomass energy crops. On the other hand, the infrastructure will likely not be 

established until there is a reasonable amount of biomass production already occurring. 

Feedstock composition will likely not favor one crop over the other, since research has shown 

that most biomass feedstocks have similar chemical composition when harvested at 

physiological maturity (Lee et al., 2007). The decision on which crop to produce will likely 

depend on a decision of which crop will best fit into a producer’s crop rotation, provide reliable 

and stable biomass yields, and produce significant positive net returns. Table 1.1 summarizes 

some of the major advantages and limitations to producing these three crops and provides a 

framework for helping to select the right crop for the right situation. It must be noted that a 

similar summary table has been published comparing C4 rhizomatous grasses, switchgrass, and 

M. x giganteus (Heaton et al., 2004).
4

One major distinction between these crops is that M. x giganteus and switchgrass are 

perennial crops and sorghum is an annual crop. For Midwest farmers, an annual crop like 

sorghum may be a front runner candidate, at least in the short term because it will more easily fit 

into current corn and soybean crop rotations and does not require committing land to production 

for multiple years like perennial switchgrass and M. x giganteus. As energy markets and a stable 

bioenergy industry infrastructure are developed, perennial switchgrass and M. x giganteus may 

provide long term options, where contracts can support committing farm ground to perennial 

crop production. One major advantage of these two perennial crops is that they do not require 

planting every year and may be well suited to soil that is less productive for corn and soybean 

production and highly erodible for annual row crop production. There are also other advantages 

and limitations associated with perennial vs. annual bioenergy crop production. One of the 

limitations of switchgrass and M. x giganteus production is the duration period required to reach 

maturity and become fully productive. Often, establishment year yields are non harvestable, 

meaning that establishment year yields are minimal and harvesting is not justified. In most 

cases, M. x giganteus takes at least three years to become fully productive (Miguez et al., 2008), 

while switchgrass may become fully productive in as few as two years (Grassini et al., 2009; 

Schmer et al., 2010), but it usually requires three years (Parrish and Fike, 2005; Heaton et al., 

2004; Schmer et al., 2010). Because establishment year yields of M. x giganteus and switchgrass 

are usually non-harvestable, a compromise between waiting for full productivity and receiving 

some economic benefit during establishment year is the use of a companion crop. Studies 

involving corn and sorghum-sudangrass companion cropped with switchgrass have 

simultaneously resulted in harvestable corn and sorghum-sudangrass crops and successfully 

established switchgrass stands (Hintz et al., 1998; Cossar and Baldwin, 2002). Sorghum requires 
5

planting every year but energy sorghum hybrids can be planted on the same row spacing as corn 

(0.76 m) using corn planting equipment. This provides an advantage over switchgrass which 

generally requires narrower spacing and the need for equipment that can plant small seeds on 

narrow row spacing, and M. x giganteus which requires vegetative propagation (Pyter et al., 

2009). 

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6

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Heaton, E., T. Voigt, and S.P. Long. 2004. A quantitative review comparing the yields of two 

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Hintz, R.L., K.R. Harmoney, K.J. Moore, J.R.George, and E.C. Brummer. 1998. Establishment 

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7

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R.W. Mullen. 2004. Switchgrass Response to Harvest Frequency and Time and Rate of 

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Production in Switchgrass across the United States: Database Description and Determinants 

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8

TABLES

Table 1.1. Summary†of several major advantages and limitations of growing and using switchgrass, M. x 

giganteus, and sorghum (forage sorghum or energy sorghum) as biomass feedstock crops. Emphasis is placed on 

growing these crops in the Midwest. A positive sign (+) represents a positive characteristic or advantage, while a 

negative sign (-) represents a negative characteristic or limitation. A 0 represents a neutral or undecided basis. 

Superscripts are included where particular advantages and limitations are referenced.

Characteristics/attributes Switchgrass M. x giganteus Sorghum 

High yielding biomass potential +

b, j, k, l +a, b, e +g, p, q

Low energy inputs (fertilizer) 0

d

0

s, t, u

-

r

Environmental benefits +

c

+ -

Annual nutrient recycling +

b

+

b

-

Breeding improvement potential +

c, i, m +e

+

g

Population genetic diversity +

c, n

-

a

+

g

Market prices

-

f

-

f 0

Ease of establishment 0

o

-

a

+

Non Invasiveness + + +

Perennial +

i

+ -

Annual

- - +

Broad adaptation +

i

- +

h

C4 photosynthetic + + +

† A similar summary table was published in Heaton et al., 2004, and some similar information is reported here. 

a) Lewandowski et al. (2000)

b) Heaton et al. (2004)

c) Sanderson et al. (2006)

d) Parrish and Fike, 2005

e) Heaton et al. (2008b)

f) Khanna et al. (2008)

g) Rooney et al. (2007)

h) Rooney and Aydin, 1999

i) Gonzalez et al. (2009)

j) Thomason et al. (2004) 

k) Sladden et al. (1991)

l) Kiniry et al. (1999)

m) Bhandari et al. (2011)

n) Narasimhamoorthy et al. (2008)

o) Sanderson et al. (1996)

p) McCollum et al. (2005)

q) Miller and McBee, 1993

r) Sorghum Management Guide, 2010

s) Christian et al. (2008)

t) Ercoli et al. (1999)

u) Cadoux et al. (2011)
9

CHAPTER 2

Management and Environmental Factors that Affect Switchgrass Biomass Production

ABSTRACT

Switchgrass (Panicum virgatum L.) dry biomass yields can be largely impacted by 

multiple environmental factors and field management practices factors that must be understood 

so that when switchgrass is grown as a bioenergy crop, proper management practices are 

implemented. The objective of this study was to compile an extensive database regarding 

switchgrass biomass production and perform a meta-analysis to evaluate the effects of growing 

region, spring precipitation, growing season, ecotype, harvest frequency, harvest timing, and 

nitrogen rate (N) rate on dry biomass yields. Data from 106 sites and 45 studies were gathered 

from the eastern two thirds of the U.S. and southeastern Canada. A random coefficients model 

was implemented to describe switchgrass biomass N response and other environmental and 

management factors were incorporated into the model. The linear N rate (P < .0001), quadratic 

N rate (P = 0.0363), region (P < .0001), spring precipitation (P = 0.0001), growing season (P = 

0.0466), ecotype (P < .0001), harvest timing (P = 0.0771), spring precipitation x ecotype (P = 

0.0092), spring precipitation x growing season (P = 0.0007), linear N rate x ecotype (P = 0.064), 

and region x harvest timing (P = 0.0817) effects were all significant. Results revealed that N 

response varies from site-to-site and the critical N fertilizer rate (Max N) required for maximum 

biomass yield may be quite higher than the economical optimum N rate (EONR). Switchgrass 

grown in the lower and upper central U.S. regions produced greater biomass yields than 

switchgrass grown in the north or south, and the lowland ecotype yielded better than the upland 

ecotype, but this depended on the region.
10

INTRODUCTION

Volatile energy markets, high prices, and finite supplies are reasons for the USA to shift its

reliance on oil and gas to more renewable sources of energy and liquid transportation fuel, 

especially biomass based energy (Heaton et al., 2008). The USA accounts for 25% of the 

world’s oil consumption, primarily to fuel the transportation and shipping sectors of its economy 

(Biomass Research and Development Board, 2008). As part of the U.S. Energy Independence 

Security Act of 2007 a Renewable Fuels Standard (RFS) mandates the production of 136 billion 

liters of biofuels by the year 2022 (Energy Independence and Security Act, 2007). Of this 136

billion liters, 79 must be from advanced biofuels (not corn starch) and 60 must come from 

cellulosic feedstock sources. Advanced biofuels or cellulosic biofuel crops like switchgrass 

(Panicum virgatum L.) provide an opportunity to grow and utilize a renewable source of energy, 

and can help to meet the mandate. 

Switchgrass has received a great deal of attention among researchers and producers 

because of its use as a forage crop and its potential as an herbaceous bioenergy crop. 

Switchgrass is a warm-season perennial grass that is native throughout much of North America 

east of the Rocky Mountains, and is most abundant east of 100°W (Vogel, 2004; Samson and 

Omelian, 1994). It has been the subject of much research in the USA since the early 1990s when 

the Department of Energy evaluated a wide variety of herbaceous species across a range of 

environments and identified it as a ‘model’ bioenergy feedstock (McLaughlin and Kszos, 2005; 

Wright and Turhollow, 2010). Switchgrass has many desirable characteristics that make it 

suitable for feedstock production. Among them, the ability to grow on marginal and disturbed 

sites, provide environmental and ecosystem services, require relatively low moisture and nutrient 

inputs, and produce relatively high and reliable dry biomass yields across a wide range of 
11

environments (Wright and Turhollow, 2010; Parrish and Fike, 2005; Mitchell et al., 2008; 

Bransby et al., 1998; Sanderson et al., 1996; Sanderson et al. 2006; Lemus and Parrish, 2009). 

Moreover, Gonzalez et al. (2009) described several traits that herbaceous bioenergy crop species 

should posses, including not directly competing with food crops for land and resources, 

producing reliable yields in a broad range of environments under limited inputs, being perennial, 

and having the potential for improvement through breeding. Switchgrass possesses each of these 

characteristics.

Several reviews (Bransby et al., 1998; Parrish and Fike, 2005; Lemus and Parrish, 2009; 

Mitchell et al., 2008; Sanderson and Adler, 2008; Sanderson et al., 1996; Sanderson et al., 2006a; 

Wright and Turnhollow, 2010) and meta-analytical (Heaton et al., 2004; Wang et al., 2010; 

Wullschleger et al., 2010) articles have been published regarding switchgrass production. From 

a production perspective, dry biomass yields among switchgrass cultivars are highly variable. In 

a database compilation of 39 studies ranging across much of the eastern two-thirds of the US, 

Wullschleger et al. (2010) reported yields ranging from 1 Mg ha-1to 39.1 Mg ha-1. Biomass 

yields can be influenced by the climate, location and geography of the experiment, management 

practices, and site-to-site conditions (Johnston et al., 2009; Wang et al., 2010). These factors 

must be considered to maximize biomass yields and maintain stand longevity. From the early 

1990s through the present, much research has focused on cultivar and ecotype adaptation and 

evaluation, harvest management, and nitrogen and fertilizer response (Table 2.1). 

The overall purpose of this study was to perform a meta-analysis of important factors in 

switchgrass production that influence dry biomass yield and was accomplished by collecting and 

synthesizing data from the literature of independent studies (Cooper et al., 2009; Gurevitch and 

Hedges, 1999; Gates, 2002). A random coefficients model was then implemented to account for 
12

the variability among sites (St-Pierre, 2002) and describe the effects of different factors on 

switchgrass biomass yield. Wullschleger et al. (2010) recently performed a statistical analysis of 

switchgrass and various factors affecting biomass yields, covering 39 different studies from 

across the USA. Their study evaluated site location, stand age, plot size, cultivar, crop 

management, biomass yield, temperature, precipitation, and ecotype, and concluded that the most 

important predictors of biomass yield were ecotype, temperature, precipitation, and N 

fertilization. To further add to the understanding of the these factors, we compile a more 

extensive database of 106 sites from 45 studies throughout North America, which includes many 

of the studies that were part of previous meta-analysis (Heaton et al., 2004; Wang et al., 2010;

Wullschleger et al., 2010) as well as additional studies that were either missed or published 

during the review process of these meta analyses (Table 2.3). A quadratic function is the most 

common functional form that has been evaluated in switchgrass N response trials (Muir et al., 

2001; Heggenstaller et al., 2009; Lemus et al., 2008a; Haque et al., 2009; Vogel et al., 2002) and 

we suspected that a quadratic function would best describe the relationship between biomass 

yield and N rate, but that environmental and site differences would result in variation in the 

strength and magnitude of the N response observed from site-to-site (i.e. intercept and slope 

would vary from site-to-site). We hypothesized that selection of proper ecotype (and specific 

cultivars within ecotype) would significantly affect biomass yield potential in different regions of 

the USA and southern Canada. We also hypothesized that harvest timing and number of annual 

harvests play an important role in affecting dry biomass yields. The database resulting from our

literature search contains information on region of growth, spring precipitation, growing season, 

ecotype, number of annual harvests, harvest timing, and annual N application rate. Following 

the compilation of this database, the specific objectives of the analysis were to develop a random 
13

coefficients model that best describes the curvilinear relationship between biomass yield and 

annual N application rate and then incorporate other selected factors into the model that have a 

significant influence on dry biomass yield. The approach in our statistical analysis that 

distinguishes it from previous analysis (Heaton et al., 2004; Wang et al., 2010; Wullschleger et 

al., 2010) is that it includes a quantification of the curvilinear N fertilization response of

switchgrass. The N responses were quantified for specific regions of the USA and southern 

Canada that were categorized into groups of relatively similar growing season length, 

temperatures, day length, and winter freezing conditions. Spring (Apr. through Jun) precipitation 

rather the growing season or annual precipitation was used as a covariate in the model because 

early season water availability can have an important impact on biomass yield (Cassida et al., 

2005) and early season precipitation has been shown to be a good predictor of biomass yield 

(Fike et al., 2006b; Lee and Boe, 2005).

MATERIALS AND METHODS

Database Compilation and Description

A literature search of primary research was conducted using the ISI Web of Knowledge 

(ISI, Philadelphia, PA) electronic database, and through location of studies included in the 

references. Switchgrass is a multi-purpose crop and has been grown for livestock forage, for 

biomass, as vegetative filter or streamside buffers, and as prairie or restorative plantings (Parrish 

and Fike, 2005). Switchgrass grown for these different purposes can be managed quite 

differently and in order make more meaningful comparisons, only data coming from studies that 

focused on switchgrass grown as a biomass crop or that were managed similar to switchgrass 

grown for biomass were included. Only literature published since 1990 measuring switchgrass 

dry biomass yields were included in the database using data gathered from the USA and southern 
14

Canada. Switchgrass literature published before 1990 focused primarily on switchgrass grown 

as a forage crop, whereas most literature on switchgrass production since 1990 has focused on 

switchgrass grown as a biomass crop. In addition to the criteria just described studies were 

excluded from our database if dry biomass yields were averaged across N rates or ecotype, if 

biomass was harvested more than twice a year or studies were un-replicated. Studies that 

averaged biomass yields harvested at different times, meaning biomass harvested at peak 

biomass (before physiological maturity) or at the end of the growing season once the crop 

reached physiological maturity or following a post killing frost, were also excluded. Studies in 

which switchgrass plants were transplanted rather than seeded were excluded from the database 

unless the plants were in the second growing season or beyond, given the crop’s ability to spread 

out, fill in stand gaps, and achieve similar yields across a wide range of plant spacings (Parrish 

and Fike, 2005). Also, before the statistical model and predictions were developed, several 

biomass yield values from Thomason et al., 2004, and Sanderson et al., 2001 receiving annual N 

application rates ranging from 322 to 896 kg N ha-1 were removed. There were very relatively 

few observations in this N application range, and the N application rates from all of the other 

studies were less than 300 kg N ha-1, thus annual N application rates for each site were limited to 

a range between 0 and 300 kg N ha-1.

In the database, studies and experiments were separated into individual sites in order to 

disaggregate varying responses to annual N application. In some cases the reported biomass 

values specified for a particular site were averaged across more than one location within a 

particular study, where it was not possible to separate the studies by location. Cultivars, 

varieties, experimental lines, and synthetic lines (each referred to as cultivars) were classified 

into two categories, upland and lowland ecotype (Table 2.2). The assignment of most upland or 
15

lowland ecotype classifications were obtained from references cited in Table 2.2. The reported 

harvest time varied from study to study, so we classified the final biomass harvest into two 

categories: peak standing crop (PSC) or end of season (ES). These classifications were assigned 

according to harvest and morphology information reported in the individual studies. Peak 

standing crop refers to harvests at or near peak biomass, which usually occurred at or near 

anthesis and before physiological maturity. The ES harvest timing is associated with harvests 

occurring after a killing frost or once the crop had begun to senesce and field dry. 

Where possible, spring precipitation (April through June) values were obtained from the 

studies when reported, or were estimated from the long-term averages when specific seasons of 

precipitation were not reported. In cases, where no precipitation data was reported, spring 

precipitation was obtained from the North American Regional Reanalysis (NARR) database 

(Mesinger et al. 2006). A few observations that utilized irrigation in any specific year were 

excluded from the database and statistical analysis so that recorded spring precipitation levels 

were not confounded with applied irrigation in the statistical analysis.

Growing seasons were divided into 3 categories: establishment (growing season 1), 

maturation (growing season 2), and production (growing seasons >=3). Growing seasons >=3 

were grouped together, primarily because the number of growing seasons >=3 ranged from 3 to 

26 year old stands, and insufficient data were available to provide reliable estimates for each of 

these individual growing seasons. It was assumed that by the time the crop reaches three years 

of growth, the crop is fully productive and potentially achieving its maximum yields (Parrish and 

Fike, 2005; Heaton et al., 2004; Schmer et al., 2010). In some studies included in the database, 

biomass yield was reported as an average of growing seasons 2 and 3 (Casler et al., 2004; Vogel 

et al., 2002, Hopkins et al., 1995) and sometimes as an average of growing seasons 2, 3, and 4 
16

(Casler et al., 2007; Haque et al., 2009). Rather than disregard these data, we categorized these 

data into growing seasons >=3. In these cases we assumed that the authors were reporting data 

from stands that were fully or near fully productive during their second season (Grassini et al., 

2009) as it has been shown that switchgrass stands can reach their yield potential in as few as 

two years (Schmer et al., 2010).

Within the database, the data was divided into 4 regions or areas of growth that were 

classified as: North Region (NR), Upper-Central Region (UCR), Lower-Central Region (LCR), 

and South Region (SR) (Fig. 2.1 and Table 2.3). These regions were created based on

information from the USDA plant hardiness zone map and latitude. Fig. 2.1 shows the sites 

categorized by region overlaid on the USDA plant hardiness zone map. The allocation of the 

studies into the 4 regions was fairly balanced with the NR, UCR, LCR, and SR containing data 

from 29, 24, 31, and 22 sites and locations, respectively (Table 2.3). In this way we simplified 

the range of production into groups of data originating from areas with relatively similar growing 

season lengths, day lengths and photoperiods, and winter freezing conditions (Fig. 2.1). Data 

from some regional scale experiments (Heaton et al., 2008; Casler et al., 2004; Casler et al., 

2007; Adler et al., 2006; Schmer et al., 2010) were split into 2 or more regions depending on the 

geographical spread of the study sites (Table 2.3). Several data points in the NR, coming from 

Casler et al., 2004 and Madakadze et al., 1998b, were excluded from the analysis because they 

were the only studies reporting lowland ecotype yields in this region, producing little information 

for prediction of lowland ecotype biomass yields in the NR. Most other studies in the NR, did 

not plant lowland cultivars, most likely because of their susceptibility to winter kill in northern 

environments.
17

In total there were 106 sites from 45 studies totaling 1476 observations in the database. 

Forty-one of these studies came from the eastern two thirds of the U.S. with the remaining four

studies coming from southeastern Canada. All studies originated from peer-reviewed journal 

articles, except Fuentes and Taliaferro (2002) which was published in a conference proceeding. 

Factors considered in the statistical analysis were: region (NR, UCR, LCR, SR), spring 

precipitation (April through June), growing seasons (1, 2, >=3), ecotype (lowland or upland), 

number of annual harvests (1 or 2), harvest timing (PSC or ES), and annual N application rate. 

Preliminary Statistical Analysis

Cultivar means, standard error of the mean (SEM), minimum, and maximum dry biomass 

yields were calculated using the MEANS procedure of SAS (SAS Institute, 2007) for growing 

season 2 and growing seasons >=3 categorized by region (Table 2.4). Data exploration, 

graphing, and data manipulation was performed using the graphical interface and descriptive 

statistical packages of R statistical software (R Core, v. 2.12.1, 2010), in particular the ‘graphics’ 

v.2.12.1 and ‘lattice’ v.0.19-13 packages. Before statistical analysis and development of model, 

biomass yields were averaged across their respective ecotype within region, site, spring 

precipitation, growing season, and number of annual harvests. Based upon these averages, mean 

ecotype values and their respective SEM were then calculated.

Statistical Analysis and Model Development

A ‘random coefficient model’ was used to describe the response of mean switchgrass dry 

biomass yield (averaged across ecotype within region, site, spring precipitation, growing season, 

and number of annual harvests) to N rate. This allowed unique N response curves to be 

estimated for each site and was necessary due to site-to-site conditions that created variation in 

the response of dry biomass to N rate (Aitkin et al., 2009; Littell et al., 2006; St-Pierre, 2002). 
18

The first step was to develop the basic model structure by considering the linear and quadratic 

components of N rate. Three models were tested each model included fixed linear and quadratic 

components of N rate ( . In addition, the first model also had a random intercept. The second 

model also had a random intercept and random linear and quadratic components of N rate. The 

third model also had a random intercept and a random linear component of N rate, but no random 

quadratic component of N rate. Akaike Information Criterion (AIC) was used to compare these

three models, and favored the third model, which had lower a lower AIC value (3480) than the 

first (3491) and second (3486) models. Each of these models resulted in significant fixed linear 

(P <.0001) and fixed quadratic (P 0.0674) N rate components. The third model that was 

selected takes on the following basic form:

 

  , [eq. 1]

 , , 

 

 

where dry biomass yield ( depends on N rate ( , the 

 mean biomass value within the  

site and is a function of the fixed ( and random ( intercept, fixed ( and random ( linear 

N rate effects, and fixed quadratic N rate effect ( . The random intercept ( depends on site

( and the random linear N rate ( effect which enters linearly into the model. The model 

assumes the random effects have a mean of zero and a general variance-covariance matrix, (i.e. 

 for random intercept and for the random linear N rate effect), and that the errors ( have 

a mean of zero and common variance. Once the basic form of this model was developed, a 

backward elimination method was implemented to investigate the fixed main effects of region, 

spring precipitation, ecotype, growing season, number of annual harvests, and harvest timing, 

and interaction effects of region x spring precipitation, ecotype x spring precipitation, growing 

season x spring precipitation, linear N rate x ecotype, quadratic N rate x ecotype and region x 
19

harvest timing. Backward stepwise searches are recommended over forward stepwise searches 

when the number of potential predictor variables is relatively small as is the case here and allows 

each potential predictor variable to be considered under the premise that it has been adjusted for 

all other potential predictor variables (Kutner et al., 2004). In the first step each of the variable 

and interactions listed above were simultaneously investigated. The following non-significant 

terms were then removed if their p-values were greater than α = 0.1: number of annual harvests 

(P = 0.6591), region x spring precipitation (P = 0.1951), and quadratic N rate x ecotype (P = 

0.6411), and growing season x ecotype (P = 0.6429). The next step involved reanalyzing those

variables that were significant in step one less the non-significant variables in step one. No 

further terms were eliminated, and based upon these results a final model was established (Table 

2.5) from which model predictions were calculated. Other suspected interactions (e.g. region x 

ecotype) were not included in the analysis because when investigated, resulted in model 

singularity, due to the elimination of the lowland ecotype from the NR. Statistical analysis was 

performed using a linear mixed-effect model in the ‘nlme’ v.3.1-97 package of R statistical 

software (R Core, v. 2.12.1, 2010). Residuals were examined for normality and the assumption 

of common variance by inspection of normal quantile and residual plots. Individual site 

predictions based upon the final model were plotted to show visual agreement between observed 

yield and model predictions (Fig. 2.3). Several unusually high mean biomass yields were 

deemed outliers, when visually assessing the residual plots. These values were not removed 

from the analysis because they did not modify the results of the model (Table 2.5), when the 

model was rerun without them. Furthermore, they may represent the potential of the switchgrass 

crop or potential sampling error, and were retained in the model. The critical rate of N fertilizer 

required to achieve maximum dry biomass yield (Max N) and ex post economic optimum N rates 
20

were calculated only for sites which reported biomass yields across at least four N rates and this 

included 14 sites. Coefficients were extracted from eq. 1, and do not reflect the fixed main 

effects or investigated interactions shown in Table 2.5 other than the linear N rate and quadratic 

N rate effects. The critical rate of N fertilizer (Max N) required to achieve maximum biomass 

yield was calculated using the following equation:

 

 

 

[eq. 2]

Where depends on the 

 

site and is the random linear N rate coefficient from eq. 1, 

and is the fixed quadratic coefficient from eq. 1. Based upon the price of Mar. urea (46% N) 

from 2006 to 2011 (USDA/ERS, 2011), three prices of urea ($362 ton-1($328.3 Mg-1), $448 ton1

($406.3 Mg-1), and $552 ton-1($500.6 Mg-1) and respective unit prices of a kg of N ($0.87 kg-1

N, $1.07 kg-1 N, and 1.32 kg-1 N), economical optimum N rates (EONR) were calculated by 

considering three theoretical prices of a Mg of dry biomass: $35 Mg-1, $50 Mg-1, and $65 Mg-1

(Table 2.6). These EONR were calculated ex post (after the fact) using the following equation:

 

 

 

 

 

 

[eq. 3]

where is the cost of N fertilizer ($ kg-1), is the price of dry biomass ($ Mg-1), is the 

random linear N rate coefficient from eq. 1, and is the fixed quadratic coefficient from eq. 1. 

RESULTS AND DISCUSSION

Preliminary Results

Mean biomass yields (± SEM) across all regions and both ecotypes were the lowest in 

growing season 1 (6.6 ± 3 Mg ha-1), which increased to 9.1 ± 5.5 Mg ha-1in growing season 2

and 10.9 ± 5.2 Mg ha-1in growing seasons >=3. This is in agreement with the literature, which 

reports that the necessary time for switchgrass to achieve full productivity varies, but is generally 
21

three years (Parrish and Fike, 2005; Heaton et al., 2004; Schmer et al., 2010), although 

switchgrass stands may become fully or near-fully productive during their second season 

(Grassini et al., 2009; Schmer et al., 2010). Factors that influence establishment, including seed 

quality, seedbed preparation, planting date, planting rate, and planting depth, can effect the time 

needed to reach full productivity (Masters et al., 2004). Moreover, low stand densities achieved 

during growing season 1 tended to result in lower biomass yields in growing season 2 than those 

having higher stand densities (Schmer et al., 2006).

Overall, the lowland ecotype produced greater yields than the upland ecotype. In 

growing season 2, ecotype mean biomass yields (± SEM) were greater for the lowland ecotype 

(11.1 ± 6.1 Mg ha-1) than for the upland ecotype (6.7± 3.2 Mg ha-1). Similarly, in growing 

seasons >=3, ecotype mean biomass yields (± SEM) were greater for the lowland ecotype (12.6 ± 

5.4 Mg ha-1) than for the upland ecotype (9.3 ± 4.3 Mg ha-1). 

Switchgrass yields were the greatest when grown in the two central regions (Fig. 2.2) 

with mean biomass yields (± SEM) averaged across growing season 2 and growing seasons >=3 

the greatest in the LCR (13.4 ± 4.5) and UCR (10.1 ± 4.3) when compared to the SR (9.3 ± 5.7) 

and NR (7.3 ± 3.1). Mean biomass yield values for the different regions were highly variable 

with large ranges: NR (1.1 to 14.5 Mg ha-1), UCR (2.5 to 26 Mg ha-1), LCR (4.6 to 33.4 Mg ha1

), and SR (0 to 30.4 Mg ha-1). Mean biomass yields of zero in the SR came from Cassida et al. 

(2005) which reported negligible yields at or near zero in LA in which stand densities of the 

upland ecotype had declined to zero or near zero after two years of production resulting in 

biomass yields equal to 0 Mg ha-1. In the NR, mean yields were less variable than in the other 

regions and the greatest mean yields in this region, reaching yields greater than 13 Mg ha-1were 
22

achieved in SD, WI, and Montreal, QC (Boe, 2007; Casler and Boe, 2003; Madakadze et al., 

1998b). 

Although mean biomass yields among the four regions were quite variable (Fig. 2.2), the 

lowland ecotype produced greater yields than the upland ecotype, especially in the LCR, where 

mean yields greater than 20 Mg ha-1 were achieved by lowland cultivars Alamo and Kanlow in 

TN, VA, and OK (Fike et al., 2006a; Fike et al., 2006b; Thomason et al., 2004). In the UCR, 

there were relatively few lowland mean biomass values, although lowland yields reached greater 

than 14.5 Mg ha-1(Vogel and Mitchell, 2008) and up to 21 Mg ha-1 (Casler et al., 2004) in Mead, 

NE.

Mean, SEM, and median calculations of individual cultivars indicate that biomass yields 

were greatly impacted by the region in which they were grown (Table 2.4). For example, Cavein-Rock (Table 2.2) is a commonly planted cultivar that has been evaluated in experiments in 

each of the regions described in this analysis. Cave-in-Rock mean yields, while quite variable,

were greatest when grown in the two central regions (UCR and LCR), achieving mean yields (± 

SEM) of 11.2 ± 5.3 in the UCR and 12.5 ± 3.9 in the LCR for growing seasons >=3 (Table 2.4). 

However when grown in the NR or SR (both distant from its origin), its mean yields decreased, 

dropping to 4.4 ± 3 in the SR and 8.5 ± 5 in the NR (Table 2.4).

Ecotype, Region, and Growing Season

The effects of region, ecotype, and growing season were each significant (Table 2.5). 

The significant effect of growing season is related to the time it takes for switchgrass to reach 

full productivity (maximum biomass yields), while the effect of ecotype appears to be dependent 

on region and the cultivar or ecotype being produced. Due to this we suspected that an ecotype x 

season interaction was also significant. However, when the ecotype x season interaction was 
23

included in the model it resulted in model singularity (an indication of the unbalanced nature of 

the data and potential model over parameterization), so we were unable to estimate its effect. 

Overall, the lowland ecotype had significantly greater biomass yields than the upland ecotype (P

< .0001) (Table 2.5). 

The overall higher biomass yields of the lowland ecotype compared to the upland ecotype 

likely has to do with the morphological differences between the two. Compared to the upland 

ecotype, the lowland ecotype is adapted to longer growing seasons with reduced photoperiod and 

lowland cultivars generally produce taller plants with thicker leaves and stems, have fewer tillers 

with greater tiller diameter, and generally have more of a ‘bunch form’ growth pattern as 

opposed to a ‘sod formation’ growth pattern (Parrish and Fike, 2005; Cassida et al., 2005). 

Although not observed in this study, variation in the morphology of switchgrass and the impact 

of environment has tended to produce an ecotype x environment interaction in experimental 

studies (Boe and Casler, 2005; Cassida et al., 2005; Casler and Boe, 2003; Casler, 2005). 

Moving cultivars south of their origin hastens reproductive development and the end of 

vegetative growth, while moving populations north of their origin can prolong vegetative growth 

(Parrish and Fike, 2005; Vogel, 2004). This potentially increases biomass yields, but Casler et 

al. (2007) indicated that movement of switchgrass populations north or south of their origin 

should be limited to no more than one USDA plant hardiness zone due to risk of winter kill. 

Two switchgrass cultivars, Cave-in-Rock, originating from ~38° N in Southern IL and OK-NU-2 

originating from 35° N latitude in Oklahoma, grown in Mandan, ND (46°48’ N) were severely 

affected by the northern movement from their origin (Berdahl et al., 2005). In the span of 4 

years their survival percentages decreased from near 100% to 20 or 30%. Other studies have 

also evaluated switchgrass cultivars in regions where adaptation is unlikely, resulting in large 
24

yield variation, and often low yields. For example, a TX study evaluated several cultivars,

including Alamo (a cultivar adapted to Texas) and Summer (a cultivar originating in the northern 

Great Plains) (Sanderson et al., 1999b). Summer is not well suited to biomass production in TX

producing dry yields as low as 1.75 Mg ha-1, while Alamo produced 17.59 Mg ha-1in the same 

study. 

Spring Precipitation

The spring precipitation x growing season, spring precipitation x ecotype, and 

precipitation effects were all significant (Table 2.5). The lowland ecotype generally produced 

greater yields than the upland ecotype under similar spring precipitation levels (Fig. 2.4). This 

can be partially explained by the adaptation differences of the different ecotypes. Cultivars 

within the lowland ecotype are best adapted to more southern latitudes and lower wetter areas, 

while cultivars within the upland ecotype are better suited to more northern latitudes and mesic 

environments with moderate soil moisture conditions. Because cultivars within the upland 

ecotype are generally more adapted to drier environments, they are typically less sensitive to dry 

conditions than cultivars within the lowland ecotype (Vogel, 2004; Parrish and Fike, 2005; 

Porter, 1966). 

As the crop matures from growing season 1 to growing seasons >=3, similar precipitation 

levels result in higher biomass yields. Precipitation has limited switchgrass growth in the 

Northern Great Plains where annual levels have been as low as 193 mm (Lee and Boe, 2005), 

and in droughty TX conditions where stand loss has been threatened (Cassida et al., 2005). Total 

annual precipitation is probably not as important as the timing and number of the precipitation 

occurrences (Wullschleger et al., 2010). Fike et al. (2006b) showed that biomass yields are 
25

strongly influenced by early season rainfall, while Lee and Boe (2005) reported that early season 

(April-May) rainfall is a good predictor of biomass yield. 

Nitrogen Response

The biomass yield response to applied N was quite variable (Fig. 2.3 and Table 2.5) and 

consequently the linear N rate x ecotype, linear N rate, and quadratic N rate effects were all

significant (Table 2.5). When predictions were made on a regional level (Fig. 2.5), the predicted 

N response curves for lowland and upland ecotypes were slightly different, with a steeper linear 

component for the lowland ecotype than the upland ecotypes. This is likely because of the 

greater number of high mean biomass yields at N rates greater than 100 kg ha-1for the lowland 

ecotype in the SR and LCR compared to the upland ecotype, which caused the linear relationship 

between biomass yield and N rate to be slightly more positive (Fig. 2.5). In Fig.’s 2.5 and 2.6,

several mean values stand out in the SR and LCR with particularly high biomass yields (>= 28 

Mg ha-1). These mean values (33.4, 29.5, 28.6, 30.4, and 29.4) come from studies with reported 

biomass yields reaching up to 36.7, 34.06, and 39.5 Mg ha-1respectively from Thomason et al.

(2004), Sladden et al. (1991), and Kiniry et al. (1999). These data represent N rates ranging from 

0 to 224 kg N ha-1, 2 and >=3 growing seasons, 1 or 2 annual harvests, different final harvest 

timing, and were for lowland cultivars, Kanlow and Alamo. These high yield values data do not 

appear to be associated with any particular management factor other than being from two highyielding lowland cultivars, and were not necessarily associated with higher N application rates, 

although they do represent the high biomass yield potential of switchgrass.

Optimal N rates and associated fertilizer recommendations have varied greatly in the 

literature and maximum yields have been achieved at a wide range of N rates. Most observed 

maximum biomass yield have been achieved at rates between 112 and 270 kg N ha-1(Haque et 
26

al., 2009; Heggenstaller et al., 2009; Vogel et al., 2002; Lemus et al., 2008a; Lemus et al., 

2008b; Muir et al., 2001; Mooney et al., 2009; Mulkey et al., 2006), although maximum biomass 

yields have been achieved at lower rates (67 kg N ha-1) (Mooney et al., 2009) and much higher 

rates (448 kg N ha-1and greater) (Thomason et al., 2004; Sanderson et al., 2001). Recommended 

or optimum N rates have ranged between 56 and 168 kg N ha-1(Haque et al., 2009; 

Heggenstaller et al., 2009; Vogel et al., 2002; Lemus et al., 2008b; Muir et al., 2001; Mulkey et 

al., 2006), while the most-efficient N rates, in terms of N use (calculated as the percentage of N 

removed from the crop) have ranged between 56 an and 112 kg N ha-1(Lemus et al., 2008a; 

Lemus et al., 2008b; Muir et al., 2001). As the literature suggests, achieving maximum biomass 

yields may not be the best way to optimize fertilizer application, and in many cases, biomass 

yields slightly lower than the maximum can be achieved from much lower N rates. This further 

implies that the EONR is generally lower than the N rate required to achieve maximum biomass. 

Even though general response curves were generated from the results of this analysis for 

a specific set of management practices and spring precipitation levels (Fig. 2.5), it must be noted 

that it is difficult to make any general or even regional recommendation regarding an optimal N 

rate for switchgrass, due the random variation from site-to-site which was high (Fig. 2.3) and a 

95% confidence interval for the linear N rate coefficients was 0.019 to 0.055. Nitrogenapplication decisions are best made by considering individual site conditions, and individual site 

predictions may provide some useful information for making these decisions (Fig. 2.3). In most 

cases the sites exhibited good agreement between observed values and predicted N response 

(Fig. 2.3). Due to the nature of the quadratic model, it has been shown to predict a decline in 

crop yield with increasing levels of N fertilizer beyond Max N (Bullock and Bullock, 1994a). 

These yield decreases are often associated with excessive amounts of N application that cause 
27

problems such as lodging. The N application range for this study was constrained to a range of 0 

to 300 kg N ha-1and at most sites higher N rates did not result in observed or predicted biomass 

decreases (Fig. 2.3). There were 14 sites in the statistical analysis that reported biomass yields 

across at least four N rates. These sites were 31a, 32a, 32b, 60a, 64a, 65a 65c, 65f, 65g, 65h, 65i, 

65j, 69a, and 73a, and come from several studies (Vogel et al., 2002; Muir et al., 2001; Mooney 

et al., 2001; Sanderson et al., 2001; Schmer et al., 2010; Haque et al., 2009; Heggenstaller et al., 

2009) (Table 2.3 and Fig. 2.3). Specifically, sites 31a (Vogel et al., 2001) and 73a 

(Heggenstaller et al., 2009), showed a reliable fit to the data (Fig. 2.3) with maximum biomass 

yields being achieved at 259 kg N ha-1and 260 kg N ha-1, respectively (Table 2.6). Max N for 

these sites were calculated by setting the first derivative of the quadratic function equal to zero 

and solving for N rate. At some of these sites predicted Max N was beyond the highest level of 

actual N applied in the studies. Due to this the observed maximum yields reported in the studies 

were sometimes lower than Max N predicted in this analysis. For example, in a two-year study 

in Boone County, IA (site 73a), maximum observed biomass yields were achieved at the highest 

N rate applied in the study (220 kg N ha-1), while Max N calculated in this analysis was at 260 

kg N ha-1(Fig. 2.3 and Table 2.6). It is likely that if higher N rates had been applied in this 

study, higher observed maximum yields would also have been observed. In 13 out of these 14 

sites, the N rate (Max N) required to achieve maximum biomass ranged from 214 kg N ha-1to 

287 kg N ha-1, with the exception of site 32a at 413 kg N ha-1(Table 2.6). Economical optimum 

N rates, calculated ex post (after the fact) (Bullock and Bullock, 1994b) for these 14 sites showed 

a wide range of values, depending on the N rate price and dry biomass price ratio (Table 2.6). 

As this ratio decreased, or as N rate prices decreased and the price of dry biomass increased, 

EONR increased. 
28

Harvest Timing

Even though the number of annual harvests was not significant in the modeling process, 

it should not be concluded that this is of little importance. Harvest frequency can have a major 

impact on N use and removal, and is likely best considered in combination with harvest timing. 

Harvest timing and its associated region x harvest timing interaction were both significant (Table 

2.5). Results of the region x harvest timing interaction are variable, especially in the LCR and 

UCR (Fig. 2.6). However, in the SR, PSC harvests tended to result in greater biomass yields

than harvests at ES, but this was not the case in the NR. In fact, the opposite occurred in the NR, 

with ES harvests yielding greater than PSC harvests. In the SR, a longer growing season and 

mild winter temperatures are likely to create an environment in which PSC harvests will not pose 

a significant threat to the survival of the crop as long as increased N removal at early harvests are 

balanced with N fertilization. In the north, PSC harvests are more likely to injure the switchgrass 

stands likely because harvesting early at PSC removes carbohydates needed for winter survival

that would have been allocated below ground to the rhizome biomass had the crop been 

harvested later at ES (Mulkey et al., 2006; Smith and Nelson, 1985). This is consistent with 

recommended harvest practices. Harvesting switchgrass once at ES or after a killing frost is the 

recommended harvest practice (Parrish and Fike, 2005) unless higher levels of fertilizer, 

especially N, are applied to compensate for greater nutrient removal, since harvesting twice per 

year removes greater amounts of N than a single annual harvest (Fike et al., 2006a; Lemus et al., 

2009; Lemus et al., 2008b). Harvesting at ES allows the switchgrass plants to prepare for winter 

by remobilizing carbohydrates and nutrients to the rhizomes, thus preparing the plant to survive 

the winter, and increasing the N recycling process. This is probably most important in northern 

environments where inadequate levels of carbohydrates may result in poor winter hardiness. 
29

Harvesting early at PSC or multiple times per year can result in decreased stand longevity and 

lowered biomass yields (Casler and Boe, 2003; Monti et al., 2008; Sanderson et al., 1999a), 

unless N recommendations are made to account for N removal rates associated with these 

practices (Parrish and Fike, 2005).

CONCLUSIONS

As the literature suggests, and this analysis confirms, switchgrass yields are highly 

variable, being influenced greatly by multiple factors. In this analysis, we found that the region, 

spring precipitation (April through June), growing season, ecotype, harvest timing, and annual N 

application rate were factors that significantly affected yields. In addition, we found that 

biomass yields can also depend on multiple environment and management factors. Switchgrass 

grown in the LCR and UCR tended to produce greater biomass yields than switchgrass grown in 

the NR and SR. The lowland ecotype tended to yield better than the upland ecotype, but this 

depended on the region. Spring precipitation had a positive effect on biomass yield and 

switchgrass biomass yields had a quadratic response to N application that was similar from siteto-site, but vary in its intercept and linear components. As large-scale production of switchgrass 

commences, it becomes necessary for stakeholders and producers to have a solid understanding 

of the effects these factors can have on biomass yields, especially when selecting the most 

appropriate ecotype or cultivar for a specific region and then basing N application rates on site 

conditions, local recommendations, and implemented harvest practices. 

Acknowledgements

The authors acknowledge Bhupinder Farmaha for his aide in creating Fig. 2.1. 
30

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TABLES

Table 2.1. List†of publications on switchgrass covering several important and highly 

studied agronomic topics.

Cultivar and ecotype 

adaptation/evaluation

Harvest management‡ Nitrogen and fertilizer 

response

Alexopoulou et al., 2008 Monti et al., 2008 Boehmel et al., 2008

Berdahl., et al., 2005 Adler et al., 2006 Christian et al., 2002

Boe and Casler, 2005 Casler and Boe, 2003 Christian, 1994

Boe and Lee, 2007 Fike et al., 2006a Haque et al., 2009

Boe, 2007 Fike et al., 2006b Heggenstaller et al., 2009

Casler and Boe, 2003 Haque et al., 2009 Lee et al., 2007

Casler et al., 2004 Heaton et al., 2008 Lee et al., 2009

Casler et al., 2007 Hopkins et al., 1995 Lemus et al., 2008a

Casler, 2005 Lee and Boe, 2005 Lemus et al., 2008b

Cassida et al., 2005 Lee et al., 2007 Ma et al., 2001

Christian et al., 2002 Lee et al., 2009 Madakadze et al., 1999a

Christian, 1994 Lemus et al., 2008b Mooney et al., 2009

Fike et al., 2006a Lemus et al., 2009 Muir et al., 2001

Fike et al., 2006b Ma et al., 2001 Piscioneri et al., 2001

Fuentes and Taliaferro, 2002 Madakadze et al., 1999a Sanderson and Reed, 2000

Garten Jr et al., 2010 Madakadze et al., 1999b Sanderson et al., 2001

Hopkins et al., 1995 Reynolds et al., 2000 Shinners et al., 2010

Lee and Boe, 2005 Sanderson et al., 1999a Staley et al., 1991

Lemus et al., 2002 Sanderson et al., 2004 Stout, 1992

Madakadze et al., 1998a Sladden et al., 1991 Thomason et al., 2004

Madakadze et al., 1998b Staley et al., 1991 Vogel and Masters, 1998

Madakadze et al., 1998c Stork et al., 2009 Vogel et al., 2002

Madakadze et al., 1999a Thomason et al., 2004

Madakadze et al., 1999b Vogel and Masters, 1998

Piscioneri et al., 2001 Vogel et al., 2002

Sanderson et al., 1999b

Sharma et al., 2003

Sladden et al., 1991

Stork et al., 2009

Vogel and Masters, 1998

Vogel and Mitchell, 2008

† This is an incomplete list of publications on these topics.

‡ Harvest management encompasses harvest timing (i.e. peak biomass, after killing frost, etc...) 

and multiple harvests per year. 
40

Table 2.2. Names of 41 different switchgrass cultivars, varieties, experimental lines, and synthetic lines (each referred to as cultivars) used in analysis, 

their ecotype, upland (U) or lowland (L), and their latitude or location of origin.

Cultivar Ecotype Latitude/Location of origin Reference(s)

Alamo L near George West, TX (Southern Texas) Cassida et al., 2005; Alderson and Sharp, 1995; Sanderson et al., 1999b

Blackwell U 36°N, Northern Oklahoma; Blackwell, OK Hopkins et al., 1995; Casler et al., 2004; Alderson and Sharp, 1995; 

Sanderson et al., 1999b

Caddo U Stillwater, OK Cassida et al., 2005; Sanderson et al., 1999b

Cave-in-Rock U 38°28’N, Near Cave-In-Rock, IL (Southern Illinois) Berdahl et al., 2005; Casler et al., 2004; Alderson and Sharp, 1995

Cave-in-Rock high yield-DMD C1

(CIR HY DDM C1)

U Hopkins et al., 1995

Dacotah U 46°30’N, Near Breien, North Dakota Boe and Casler, 2005; Alderson and Sharp, 1995; Berdahl et al., 2005

Dakota U 47°N, North Dakota Madakadze et al., 1998b

EY x FF LDMDC1 U same base population as Trailblazer Redfearn et al., 1997

EY x FF LDMDC3 U same base population as Trailblazer Redfearn et al., 1997

Ey x FF high yield C3 U Hopkins et al., 1995

Ey x FF low IVDMD C1 U Hopkins et al., 1995

Forestburg U 44°N, Forestburg, South Dakota Madakadze et al., 1998b; Casler et al., 2004; Alderson and Sharp, 1995; 

Christian et al., 2002

GA992 and GA993 L Derived from Kanlow and Alamo, respectively Garten Jr. et al., 2010

Kanlow L 35°N, Central Oklahoma (Wetumka, OK) Hopkins et al., 1995; Casler et al., 2004; Alderson and Sharp, 1995;

Sanderson et al., 1999b

Kanlow N1 L Vogel and Mitchell, 2008

Kansas Native U Sladden, 1991

KYPV 9504 U West Virginia Stork et al., 2009

KYPV 9505 U West Virginia Stork et al., 2009

KYPV 9506 U West Virginia Stork et al., 2009

late synthetic U Experimental breeding line from Nebraska Sanderson et al., 1999b

Late synthetic high yield (Late syn HY) U Fuentes and Taliaferro, 2002

Late syn high yield C3 (Late syn HY C3) U Hopkins et al., 1995

Late syn high yield-DMD C2

(Late syn HY DMD C2)

U Hopkins et al., 1995
41

Table 2.2. continued

Cultivar Ecotype Latitude/Location of origin Reference(s)

NCSU-1 L Sanderson et al., 1999b

NCSU-2 L Sanderson et al., 1999b

ND3743 U 49°N, Near Upham, ND Berdahl et al., 2005; Madakadze et al., 1998b

Nebraska 28 U 42°N, Holt Co, NE Alderson and Sharp, 1995

NJ50/Carthage L North Carolina Madakadze et al., 1998b

NL 94-2, NL 93-1 L Synthetic lines from OK and southern KS Cassida et al., 2005

NU 94-2 U Synthetic line from OK and southern KS Cassida et al., 2005

OK NU-2 U 35°N, Oklahoma Agric. Exp. Stn. Berdahl et al., 2005

Pathfinder U Nebraska and Kansas Hopkins et al., 1995; Alderson and Sharp, 1995; Madakadze et al., 1998b

Pathfinder HYDMDC2 U experimental line from Pathfinder Redfearn et al., 1997

PMT-279 L Collected in south Texas Sanderson et al., 1999b

PMT-785 L Collected in south Texas Sanderson et al., 1999b

Shawnee U Southeastern Illinois, selected from Cave in Rock Boe and Casler, 2005, Berdahl et al., 2005

Shelter U 39°N, Near St. Mary's, West Virginia Madakadze et al., 1998b; Casler et al., 2004; Alderson and Sharp, 1995; 

Sanderson et al., 1999b

SL 93-1, SL 93-2, SL 93-1 L Synthetic lines from central and southern TX

SU 94-2 U Synthetic line from central and southern TX Cassida et al., 2005

Summer U 40°40’N, south of Nebraska City, NE Gunter et al., 1996; Casler et al., 2004; Alderson and Sharp, 1995; Berdahl 

et al., 2005

Sunburst U 43°N to 42°.40’N, Union County, South Dakota Berdahl et al., 2005; Madakadze et al., 1998b; Casler et al., 2004

Trailblazer U 43°N to 37°N, Nebraska and Kansas collections Berdahl et al., 2005; Vogel et al., 1991; Casler et al., 2004; Alderson and 

Sharp, 1995

WS-SB U Sterling Barrens State National Area, WI Casler et al., 2007

WS-IP U Ipswitch Prairie State Natural Area, WI Casler et al., 2007
42

Table 2.3. Publications from the database included in the analysis, their Country, Experiment number, site 

number(s) and location(s), and their Region of classification (North, Upper Central, Lower Central, and South).

For each region, information is sorted by site. 

Authors Country Experiment Site number(s) and locations

-----------------------------------------------------------------------------------North-----------------------------------------------------------------------------------------

Boe, 2007 USA 07 07a – South Shore, SD

Boe and Lee, 2007 USA 08 08a – Aurora, SD

Berdahl et al., 2005 USA 09 09a – Mandan 1, ND, 09b – Mandan 2, ND, 09c – Dickinson, ND

Lee and Boe, 2005 USA 15 15a – Dakota Lakes 1, SD, 15b – Dakota Lakes 2, SD

Boe and Casler, 2005 USA 19, 20 19a – Bristol, SD, 19b – South Shore, SD, 20a – Pierre, SD

Casler et al., 2004 USA 23 23a – Spooner, WI, 23b – Arlington, WI

Casler and Boe, 2003 USA 25 25a – Brookings, SD, 25b – Arlington, WI

Madakadze et al., 1998c Canada 37 37a – Montreal, QC

Madakadze et al., 1999a Canada 43 43a – Montreal, QC

Madakadze et al., 1998b Canada 45 45a – Montreal, QC

Madakadze et al., 1999b Canada 49 49a – Montreal, QC

Schmer et al., 2010 USA 65 65a – Munich, ND, 65b – Streeter, ND, 65c – Bristol, SD, 65d – Highmore, SD, 

65e – Huron, SD, 65f – Ethan, SD, 65g – Crofton, NE, 65h – Atkinson, NE.

Casler et al., 2007 USA 68 68a – Spooner, WI, 68b – Mandan, ND, Rosemont, MN, Marshfield, 

Lancaster, & Arlington, WI

Shinneres et al., 2010 USA 74 74a – Arlington, WI

------------------------------------------------------------------------------Upper Central-----------------------------------------------------------------------------------

Adler et al., 2006 USA 11 11b – Ligonier, PA

Casler et al., 2004 USA 23 23c – Mead, NE, 23d – Manhattan, KS

Vogel et al., 2002 USA 31 31a – Ames, IA

Heaton et al., 2008 USA 44 44a – Shabbona, IL, 44b – Urbana, IL

Hopkins et al., 1995 USA 46 46a – Mead, NE, 46b – Ames, IA, 46c – West Lafayette, IN

Vogel and Mitchell, 2008 USA 48 48a – Mead, NE

Staley et al., 1991 USA 58 58a – northern Appalachian ridge and valley province 1, PA, 

58b – northern Appalachian ridge and valley province 2, PA, 

58c – northern Appalachian ridge and valley province 3, PA

Cuomo et al., 1996 USA 59 59a – Mead, NE

Redfearn et al., 1997 USA 61 61a – Ames, IA, 61b – Mead, NE

Lemus et al., 2008a USA 63 63a – Lucas County, IA, 63b – Wayne County, IA

Schmer et al., 2010 USA 65 65i – Douglas, NE, 65j – Lawrence, NE

Casler et al., 2007 USA 68 68c – Mead, NE, Ames, IA, Columbia, MO, Dekalb, IL

Propheter and Staggenborg, 2010 USA 71 71a – Troy, KS, 71b – Manhattan, KS

Heggenstaller et al., 2010 USA 73 73a – Boone County, IA

---------------------------------------------------------------------------------Lower Central-------------------------------------------------------------------------------

Adler et al., 2006 USA 11 11a – Rock springs, PA

Fike et al., 2006b USA 13 13a – Princeton, KY, 13b – Raleigh, NC, 13c – Jackson, TN, 13d – Knoxville, TN, 

13e – Blacksburg A, VA, 13f – Blacksburg B, VA, 13g – Orange, VA, 

13h – Morgantown, WV

Fike et al., 2006a USA 14 14a – Princeton, KY, 14b – Raleigh, NC, 14c – Jackson, TN, 14d – Knoxville, TN, 

14e – Blacksburg A, VA, 14f – Blacksburg B, VA, 14g – Orange, VA, 

14h – Morgantown, WV

Evanylo et al., 2005 USA 21 21a – Wise County, VA)

Casler et al., 2004 USA 23 23e – Stillwater, OK

Thomason et al., 2004 USA 24 24a – Chickasha, OK, 24b – Perkins, OK

Fuentes and Taliaferro, 2002 USA 41 41a – Chickasha, OK, 41b – Haskell, OK

Heaton et al., 2008 USA 44 44c – Simpson, IL

Mooney et al., 2009 USA 60 60a – Milan, TN

Lemus et al., 2008b USA 62 62a – Blacksburg & Orange, VA

Sanderson et al., 2004 USA 66 66a – Rock Springs, PA

Casler et al., 2007 USA 68 68d – Stillwater, OK & Fayetteville, AR

Haque et al., 2009 USA 69 69a – Stillwater, OK

Garten Jr. et al., 2010 USA 70 70a – Milan, TN

Stork et al., 2009 USA 72 72a – Lexington, KY

-------------------------------------------------------------------------------------South---------------------------------------------------------------------------------------

Bow et al., 2008 USA 04 04a – Stephenville, TX

Cassida et al., 2005 USA 10 10a – College Station, TX, 10b – Dallas, TX, 10c – Stephenville, TX, 

10d – Hope, AK, 10e – Clinton, LA

Ma, 2001 USA 30 30a – Shorter, AL, 30b – Piedmont substation, AL

Muir et al., 2001 USA 32 32a – Stephenville, TX, 32b – Beeville, TX

Sanderson et al., 1999b USA 35, 36 35a – Beeville, TX, 35b – College Station, TX, 5c – Dallas, TX, 35d – Stephenville, 

TX, 35e – Temple, TX, 36a – Stephenville, TX, 36b – College Station, TX

Sladden et al., 1991 USA 40 40a – East Central, AL

Sanderson et al., 1999a USA 42 42a – Dallas, TX, 42b – Stephenville, TX

Sanderson et al., 2001 USA 64 64a – Stephenville, TX

Kiniry et al., 1999 USA 67 67a – Temple, TX
43

Table 2.4. Frequency/sample size (n), mean, standard error of mean (SEM), minimum (min), and maximum 

(max) biomass yields of switchgrass cultivars in growing seasons >=3 categorized by region. Values are sorted by 

mean from largest to smallest.

cultivar n/freq† mean‡SEM § min‡ max‡cultivar n/freq mean SEM min max

------------------north region, growing seasons >=3---------------- ------------upper central region, growing seasons >=3----------

NJ50/Carthage 2 13.9 0.9 13.2 14.5 Pathfinder HYDMDC2 3 16.7 6.4 12.8 24.1

Shelter 2 11.5 0.3 11.3 11.8 NL 2 15.6 7.5 10.3 20.9

Shawnee 26 10 4.6 2.9 18.9 CIR HY-DMD C1 2 14.2 1 13.5 14.9

WS-SB 2 10 1.2 9.2 10.8 SL 2 13.4 5.8 9.3 17.5

Pathfinder 13 9.8 3 4 14.4 late syn HY C3 2 12.8 2.2 11.2 14.4

Blackwell 5 9.7 3.3 4.6 13.3 EY x FF LDMDC1 2 12.7 0.8 12.2 13.3

SU¶2 9.4 2.9 7.4 11.4 EY x FF LDMDC3 2 11.7 4.3 8.7 14.8

NU¶2 8.8 2.1 7.3 10.3 SU 2 11.5 5.0 8 15

WS-IP 2 8.7 1.3 7.7 9.6 Cave-in-Rock 38 11.2 5.3 4.2 26

Sunburst 34 8.6 3.7 2.6 17.1 Pathfinder 3 11.2 3.7 7.1 14.2

Cave-in-Rock 47 8.5 5 1.4 20.6 Sunburst 3 10.4 3.2 6.9 13.3

Trailblazer 19 8 3.7 2.9 14.1 NU 2 9.9 4 7.1 12.7

NL¶2 7.6 4.2 4.6 10.6 Blackwell 2 9.9 1.8 8.6 11.2

Forestburg 21 7.1 3.2 2.3 13.5 NJ50/Carthage 16 9.6 3.0 4.4 14.1

ND 3743 2 6.6 0.6 6.1 7 Trailblazer 7 8.4 3.9 3.3 13.4

Summer 4 5.6 0.6 4.8 6.1 Shelter 4 3.2 1.3 2.1 5.1

Dakota#2 5.4 0.2 5.3 5.6 Pathfinder HYDMDC2 3 16.7 6.4 12.8 24.1

Dacotah 36 5.2 2.6 1.5 10.3 NL 2 15.6 7.5 10.3 20.9

SL¶2 5.1 1.9 3.8 6.5 CIR HY-DMD C1 2 14.2 1 13.5 14.9

------------lower central region, growing seasons >=3------------ ----------------south region, growing seasons >=3----------------

Kanlow 105 15 3.8 7.1 26.4 SL 13 15.7 4.9 9.2 27.8

Alamo 107 15 4.8 4.6 27.4 NL 13 14.6 3.9 9 20.9

PMT-279 12 14.2 4.6 7.1 21.1 Alamo 110 11.1 7 1.3 39.5

Cave-in-Rock 106 12.5 3.9 5 26.4 PMT-785 17 10.3 4.9 2.4 17.6

Shelter 70 11.9 3.4 5.3 22.5 PMT-279 19 9.9 5.8 1.8 18.9

Blackwell 13 11 3.5 6.3 20.3 NCSU-1 16 9.6 4.8 1.8 18

KYPV 9504 7 10.9 3.4 7.2 16.4 NCSU-2 16 9.6 5.2 1.9 18.7

late syn HY 12 10.9 4.0 5.8 19.2 Kanlow 20 9.4 6.4 1 23.2

Caddo 12 10.6 3.5 5.2 16.9 Summer 2 6.3 6 2.1 10.6

KYPV 9505 7 10.5 3.7 6.6 16.6 Blackwell 3 6.3 2 4.0 8

KYPV 9506 7 10.2 3.3 6.8 15.8 SU 94-2 13 5.7 3.4 0 11.3

Summer 6 9.5 3.2 5.2 14.6 NU 13 5.6 3.3 0 10.2

Shawnee 2 7.6 1.2 6.7 8.5 Late synthetic 2 5.3 2.8 3.3 7.3

Trailblazer 9 7.2 3 4.7 12.9 Caddo 18 5.2 3.1 1.3 11

Cave-in-Rock 19 4.4 3 0.6 10.4

Shelter 2 2.8 0 2.8 2.8

† Only cultivars with frequencies or sample size (n) >=2 were included. 

‡ Mean, min, and max were calculated within region across nitrogen rates (0 to 300 kg N ha-1). 

§ SEM of cultivars within region and sample size, n. 

¶ NU, NL, SU, and SL represent northern upland, northern lowland, southern upland, and southern lowland values, which are 

reported in the literature in most cases as mean values of similar cultivars. 

# Dakota was likely spelled incorrectly in the literature, and the correct spelling of cultivar name should be Dacotah. Rather 

than make the assumption that the name was misspelled, two names, and consequently two cultivars are distinguished.
44

Table 2.5. ANOVA table of significant management and environmental effects influencing dry biomass yields of 

switchgrass obtained from the literature from across the eastern two thirds of the U.S. and southeastern Canada.

Effect/Factor Num. df Den. df F-value P-value

(Intercept) 1 494 900 <.0001

Linear N rate 1 494 35 <.0001

Quadratic N rate 1 494 4 0.0363

Region 3 93 16 <.0001

Spring precipitation 1 494 15 0.0001

Growing season 2 494 3 0.0466

Ecotype 1 494 105 <.0001

Harvest timing 1 494 3 0.0771

Spring precipitation x Ecotype 1 494 7 0.0092

Spring precipitation x Growing season 2 494 7 0.0007

Linear N rate x Ecotype 1 494 3 0.064

Region x Harvest timing 3 494 2 0.0817
45

Table 2.6. Site random intercept, random linear N rate, and fixed quadratic N rate coefficients for sites that reported switchgrass biomass yields across at 

least four N rates. N rate required to achieve maximum biomass (Max N), and ex post economic optimum N rates based upon three prices of a unit of N 

($0.87 kg-1 N, $1.07 kg-1 N, and 1.32 kg-1 N) and three prices for a Mg of dry biomass ($35 Mg-1, $50 Mg-1, and $65 Mg-1). These calculations do not take 

into account the significant effects in Table 2.5 other than the intercept, linear N rate, and quadratic N rate effects. The coefficients were extracted from the 

basic model described in materials and methods before other effects were incorporated. 

-----------------------------N rate price/dry biomass price ratio ($ kg-1 N:$ Mg-1) ----------------------------

sites intercept

linear N 

rate 

coefficient

quadratic N 

rate 

coefficient

Max 

N

0.87/35

=0.025

0.87/50

=0.017

0.87/65

=0.013

1.07/35

=0.031

1.07/50

=0.021

1.07/65

=0.016

1.32/35

=0.038

1.32/50

=0.026

1.32/65

=0.020

31a 8.286677 0.0394 -0.0000761 259 96 145 171 58 118 151 11 85 125

32a 5.819987 0.062863 -0.0000761 413 250 299 325 212 273 305 165 240 280

32b 5.371916 0.036925 -0.0000761 243 79 128 155 42 102 135 0

†

69 109

60a 10.37737 0.036313 -0.0000761 239 75 124 151 38 98 130 0

†

65 105

64a 3.066783 0.042905 -0.0000761 282 119 168 194 81 141 174 34 108 149

65a 3.837839 0.043695 -0.0000761 287 124 173 199 86 147 179 39 114 154

65c 7.430547 0.032598 -0.0000761 214 51 100 126 13 74 106 0

†

41 81

65f 3.32399 0.039886 -0.0000761 262 99 148 174 61 122 154 14 89 129

65g 4.196071 0.041798 -0.0000761 275 111 160 187 74 134 167 27 101 141

65h 3.621642 0.043413 -0.0000761 285 122 171 197 84 145 177 37 112 152

65i 5.160255 0.040322 -0.0000761 265 102 151 177 64 124 157 17 92 132

65j 3.420913 0.04271 -0.0000761 281 117 166 193 80 140 173 33 107 147

69a 7.878856 0.039197 -0.0000761 258 94 143 170 57 117 149 10 84 124

73a 8.540619 0.039492 -0.0000761 260 96 145 172 59 119 151 12 86 126

† Economic optimum N rates calculated were less than zero. 
46

FIGURES

Figure 2.1. Sites and locations included in the statistical analysis of switchgrass biomass yields grouped by region and overlaid on the USDA plant hardiness 

zones for the USA (excluding AK and HI), and two Canadian provinces (Ontario & Quebec). Description of the USDA plant hardiness zones (labeled 1 to 11 on 

map) follows that explained by Vogel et al., 2005 and the average annual minimum temperature ranges (°C) are as follows: zone 1, less than -45.6, zone 2, -45.5 

to -40.0; zone 3, -39.9 to -34.5; zone 4, -34.5 to -28.9; zone 5, -28.8 to -23.4; zone 6, -23.3 to -17.8; zone 7, -17.7 to -12.3; zone 8, -12.2 to -6.7; zone 9, -6.6 to -

1.2; zone 10, -1.1 to 4.4; zone 11, greater than 4.5. Sites from Stout et al., 1992, Staley et al., 1991, and Lemus et al., 2008a were not included on map, because 

no detailed information regarding latitude and longitude of sites were provided in the papers.
47

Figure 2.2. Boxplots of mean dry biomass switchgrass yield (Mg ha-1) for each growing season, region, and ecotype. From left to right the 

plots represent growing seasons 1, 2, and >=3. The L and U in the upper part of each panel represent lowland and upland ecotypes, 

respectively, with upland and lowland ecotypes respectively in the upper and lower panels. The black dot in each boxplot represents the 

median, while open circles represent outlying values. 
48

Figure 2.3. Individual model predictions for each site included in the statistical analysis. Open circles represent 

mean switchgrass dry biomass values plotted against N rate. Solid lines represent fixed effects for individual site 

predictions, and dashed lines represent the random effect of individual sites (experimental units). The number in 

each panel corresponds to the site number in Table 2.3.
49

Figure 2.4. Switchgrass dry biomass yield (Mg ha-1) plotted against spring precipitation (April through June)

fitted with simple linear regression lines. Key showing L and U, represent lowland and upland ecotypes, 

respectively. 
50

Figure 2.5. Observed lowland and upland ecotype mean switchgrass dry biomass yield values (Mg ha-1) plotted 

against nitrogen rate (kg N ha-1) for each region. Observed mean values are only for growing seasons >=3 are 

subsetted for PSC harvest (upper figure) and ES harvest (lower figure). Dry biomass yield predictions are based 

on the model in Table 2.5 for growing seasons >=3, harvested at PSC (upper figure) or ES (lower figure), with a 

spring precipitation (April – June) level of 300 mm. Open circles represent the lowland ecotype while solid 

triangles represent the upland ecotype. The darker dashed prediction line represents the lowland ecotype while the 

lighter solid prediction line represents the upland ecotype.
51

Figure 2.6. Mean switchgrass dry biomass yield values (Mg ha-1) plotted against nitrogen rate (kg N ha-1) for each 

region, and 80% confidence ellipses grouped by harvest timing: peak standing crop (PSC) and end of season (ES). 

Solid squares and solid lines represent PSC, and open triangles and dashed lines represent ES. 
52

CHAPTER 3

Miscanthus x giganteus Productivity: The Effects of Management in Different 

Environments

ABSTRACT

Little research has been published in the USA investigating Miscanthus x giganteus (M. x

giganteus), a C4 perennial grass showing great potential as a high yielding biomass crop. 

Understanding the growth and biomass yield potential of M. x giganteus in different 

environments in the USA is critical to identifying a suitable growing region. This study 

investigated the establishment success, plant growth, and dry biomass yield of M. x giganteus

during its first three seasons at four locations (Urbana, Illinois; Lexington, Kentucky; Mead, 

Nebraska; Adelphia, NJ, referred to as IL, KY, NE, and NJ) in the USA. Three nitrogen rates (0, 

60, and 120 kg ha-1) were applied at each location each year. Good survival of M. x giganteus

during its first winter was observed at KY, NE, and NJ (79-100%), and poor survival at IL 

(17%), due to late planting and cold winter temperatures that dropped to -27°C. Site soil 

conditions, and growing season precipitation and temperature had the greatest impact on dry 

biomass yield between season two (2009) and season three (2010). This was especially true at 

NE, where ideal weather conditions, resulted in significant yield increases (p<.0001), 15.63 Mg 

ha-1to 27.32 Mg ha-1. Biomass yield decreases of 17.02 to 13.21 Mg ha-1in KY and 13.46 to 

11.37 Mg ha-1in NJ were observed between season two and three. These decreases were 

primarily related to excessive spring rain and hot dry conditions later in the growing season at 

KY, and hot dry weather, and poor soil conditions in NJ. Season three yields were positively 

correlated with end-of-season plant height ( = 0.91) and tiller density ( = 0.76). N fertilization 

had no significant effect on plant height, tiller density, or dry biomass yield at any of the sites 

during seasons two or three.
53

INTRODUCTION

Current worldwide energy instability has resulted in the investigation of alternative energy 

sources. In response, several herbaceous crops are being researched as potential sources of 

biomass feedstock that can be utilized as energy sources. One of these crops, Miscanthus x 

giganteus, is a sterile and rhizomatous perennial C4 photosynthetic plant (Lewandowski et al., 

2000) that has potential for producing high dry biomass yields (Heaton et al., 2004), exhibits 

efficient conversion of solar radiation to biomass, efficient use of nitrogen and water, and 

possesses good pest and disease management (Beale and Long, 1995; Beale et al., 1999). This 

long-lived perennial grass is a cross between M. sinensis and M. sacchariflorus (Hodkinson and 

Renoize, 2001) as confirmed by Hodkinson et al. (2002) and Swaminathan et al. (2010). M. x 

giganteus was first collected in Japan in 1935 (Hodkinson et al., 2002) and was initially planted 

as a landscape ornamental, and later a bioenergy feedstock in Europe. From the time a new crop 

of M. x giganteus is planted, 3-5 seasons are necessary for the crop to become fully established 

and be considered a mature crop that can achieve ceiling biomass yields (Miguez et al., 2008). A 

high-yielding feedstock, mature stands of M. x giganteus have been close to 40 Mg ha-1 peak 

biomass yields in some European locations (Miguez et al., 2008). A quantitative review of 

mature M. x giganteus stands across Europe reported a mean peak biomass yield of 22 Mg ha-1, 

averaged across nitrogen rates and precipitation levels (Heaton et al., 2004), while harvestable 

yields up to 25 Mg ha-1 from mature stands of M. x giganteus have been reported in areas 

between central Germany and southern Italy (Lewandowski et al., 2000). M. x giganteus is 

typically harvested during the winter or early spring once significant drying of stems and leaves, 

and translocation of nutrients from above ground plant tissue to rhizomes, has occurred. There is 

a tradeoff between harvesting early, at the end of the growing season, or late, during the winter 
54

or in the early spring. End of season or ‘peak biomass’ harvests have higher yields, but higher 

moisture levels and higher content of undesirable minerals, particularly nitrogen. Waiting to 

harvest until winter or early spring reduces yields, but the need for fertilization is reduced and 

feedstock quality is improved (Heaton et al., 2009; Lewandowski et al., 2000; Beale and Long, 

1997; Clifton-Brown and Lewandowski, 2002). Waiting to harvest until winter or early spring is 

the recommended practice and results in approximately a 33% reduction from peak biomass 

yields (Lewandowski et al., 2003), lost primarily from drying tissue and senesced leaves that 

have dropped from the stems. 

More extensive study of M. x giganteus as a bioenergy feedstock in the USA has taken 

place over the past decade, and relatively little information regarding plant growth, biomass yield 

and its response to agronomic treatments such as nitrogen, are available. At present, M. x 

giganteus biomass yields in the USA have been reported in only four publications from Illinois 

and Kansas (Heaton et al.,2008; Dohleman and Long, 2009; Propheter et al., 2010; Propheter and 

Staggenborg, 2010). Propheter et al. (2010) reported mean harvestable biomass yield increases 

of 2.7 to 11.8 Mg ha-1and 4.0 to 13.7 Mg ha-1from season 1 to season 2 for Manhattan, Kansas 

and Troy, Kansas, respectively. Heaton et al. (2008) and Dohleman and Long (2009) reported 

mean M. x giganteus, biomass yields ranging from 17.9 to 34.6 Mg ha-1in Illinois from mature 

stands.

Several current studies are in progress that will significantly expand the number of M. x 

giganteus evaluation sites throughout the USA and include much of the Midwest, areas of the 

Great Plains, and Atlantic coast regions. Data from these studies will provide valuable 

information that can be used to identify the optimal growing region for M. x giganteus. Such 

data will also provide valuable information about establishment success, responses to new and 
55

varying environments, growth patterns from season to season, and response to N fertilizer. 

Because there are limited USA sites growing M. x giganteus, scant information is available to 

suggest a suitable growing region for the grass.

It is believed that M. x giganteus should be grown in temperate climates, as a frost period 

is needed in order to mark the end of the growing season and the beginning of dormancy, which 

in turn promotes plant senescence (Pyter et al., 2009). M. x giganteus possesses winter hardiness 

traits obtained from M. sinensis (Clifton-Brown and Lewandowski, 2000), however there are still 

concerns about the ability of this crop to withstand harsh winter environments (low and 

fluctuating winter temperatures) (Clifton-Brown and Lewandowski, 2000). In northern Europe, 

testing has shown that M. x giganteus rhizomes are severely affected by temperatures < -3.4°C 

(Clifton-Brown and Lewandowski, 2000). Additionally, late-spring frosts have proven to 

negatively affect emerging and young M. x giganteus tillers (Farrell et al., 2006). The risk of 

growing this crop in some colder environments may be confined primarily to the establishment 

years, because mature stands of M. x giganteus have survived winters in IL with air temperatures 

lower than -26°C (-15°F) (personal observations).

Beyond the need to identify the optimal M. x giganteus growing regions in the USA, 

additional research is needed regarding its response to applied N. The literature shows varied 

response to applied N (Miguez et al., 2008; Lewandowski et al., 2000; Heaton et al., 2004). For 

example in Italy over 4 years M. x giganteus responded favorably to applied N up to 200 kg N 

ha-1(179 lb N acre-1) (Ercoli et al., 1999). At Rothamsted, England, M. x giganteus grown for 14 

years on a silty clay loam did not respond to cumulative applications of 60 and 120 kg N ha-1

(Christian et al., 2008). Across 14 years there was only a 5% difference in biomass yield 

between N treatments. In their study Christian et al. (2008) suggested that annual applications of 
56

7 kg P ha−1and 100 kg K ha−1 were important for soil maintenance. One major difference 

between these two studies, aside from site differences, is that the Italian study was harvested at 

peak biomass, while the English study was harvested in winter, the recommended harvest timing. 

In general it appears that yield response to applied N will occur on a site-by-site basis. It has 

also been suspected that some N may be made available from biological N-fixation (Davis et al.,

2010). Other factors that may impact N response include: soil type and quality (i.e. texture, bulk 

density, rooting depth), % soil organic matter which influences the amount of annual N 

mineralization, harvest timing, status of other soil nutrients, and the length of time since planting.

As part of the Sun Grant/U.S. Department of Energy Regional Biomass Feedstock 

Partnership, M. x giganteus was planted in replicated trials in four locations through the central 

region of the eastern half of the US. The locations ranging from west to east were Mead, NE; 

Urbana, IL; Lexington, KY; and Adelphia, NJ (referred to as NE, IL, KY, and NJ). This 

experiment began in 2008 and will be ongoing for five or more years, allowing sufficient time 

for the crop to reach maturity at each of the sites. This experiment involves various investigators 

from multiple institutions, and multiple aspects of production and sustainability are being 

evaluated. The overall objective for this experiment is to develop and analyze data regarding 

sustainability, soil C sequestration, growth, morphology, and biomass yield under different N 

rates for assessing the potential expansion of M. x giganteus as a bioenergy feedstock resource.

This specific study focuses predominately on aboveground plant material. The first three 

years of production as establishment years for the crop were considered. The specific objectives 

of this study were to: 1) establish M. x giganteus at these four locations and assess their 

overwintering capability, and 2) collect morphological, plant growth, and biomass yield data that 
57

can be used to assess the impact of temperature and precipitation, season of growth, and N rate 

on the productivity of M. x giganteus at each of these locations. 

MATERIALS AND METHODS

Site Descriptions

This field study was conducted at four university field sites: University of Illinois 

Urbana-Champaign, Urbana, IL (40°06’20” N, 88°19’18 W); University of Kentucky, 

Lexington, KY (38°07’45” N, 84°30’08 W); University of Nebraska-Lincoln, Mead, NE 

(41°10’07” N, 96°28’10” W); and Rutgers, The State University of New Jersey, Adelphia, NJ 

(40°13’31” N, 74°14’54 W). At IL, the soil is classified as a very deep, well-drained Wyanet silt 

loam (loamy, mixed, active, mesic Typic Argiudolls). The upper 30 cm of soil at this site is 

dominated by a sandy loam soil texture that transitions into a silty clay loam soil texture at the 50 

to 100 cm depth (Table 3.1). The water table at this site ranges between 61 and 107 cm in depth. 

Organic matter levels are relatively low ranging from 1.9% at the 0-10 cm depth down to 1.2% at 

the 50-100 cm depth (Table 3.1). This is a non-typical site for the east-central IL area; typical 

soils for this area are usually Drummer (very deep, poorly drained, silty clay loam soils) or 

Flanagan (very deep, somewhat poorly drained, silt loam soils) series. At KY, the soil is 

classified as very deep, well drained Maury silt loam (Fine, mixed, active, mesic Typic 

Paleudalfs) with a water table deeper than 200 cm. Percent organic matter levels at this site 

range from 4.7% at the 0-10 cm depth to 1.8% at the 50-100 cm depth. At NE, the soil is 

classified as a very deep well drained Tomek silt loam (Fine, smectitic, mesic Pachic 

Argiudolls), however this specific site is dominated by a silty clay loam soil texture (Table 3.1). 

The water table at this site is greater than 200 cm in depth and percent organic matter levels 

range from 5.1% at the 0-10 cm depth down to 1.4% at the 50-100 cm depth. At NJ, the soil is a 
58

Holmdel sandy loam (Fine-loamy, mixed, active, mesic Aquic Hapludults) with a relatively high 

water table ranging between 15 and 91 cm in depth. Percent organic matter levels at this site 

range from 2.1% at the 0-10 cm depth down to 0.8% at the 50-100 cm depth. At this site there is 

a restrictive soil layer or rather a bedrock layer between 50 cm and 80 cm depth, depending on 

the plot.

Crop Establishment

In fall 2007, rhizomes were harvested from a demonstration planting of M. x giganteus at 

the University of Illinois Landscape Horticulture Research Center, Urbana, IL. This 

demonstration was planted in 1988 using rhizomes obtained from the Chicago Botanic Garden 

(Glencoe, IL, USA). Propagation took place in University of Illinois greenhouses where 

rhizomes of approximately 25 g were planted into 9-cm square pots during winter and spring 

2008, in artificial soil mixes and grown in the greenhouse. In early-to-mid summer 2008, 1200 

potted plants were shipped to each location for hardening and transplanting. At each location, 

twelve 10 m x 10 m plots comprised of 100 M. x giganteus plants per plot were transplanted at a 

density of 1 plant m-2, with 5 m alleys between the plots. Throughout this study, the stand 

density of 1 plant m-2 was maintained, since M. x giganteus produces sterile seed and the plants 

during the duration of this study are slowly filling in the gaps between plants. Transplanting 

dates were 24 July, 20 June, 18 June, and 19 June, respectively in IL, KY, NE, and NJ. Late 

planting at the IL site occurred due to the time necessary to propagate additional M. x giganteus

plants. The plots at each location were arranged in a randomized complete block design with 

four replicates. Three N treatments (0, 60, and 120 kg ha-1) were applied at each location in each 

replicate each year beginning with establishment year (2008). During the planting year, 

irrigation and mechanical weed control were provided where necessary at each location to 
59

promote establishment of M. x giganteus. In spring 2009, the number of surviving plants from 

those transplanted in 2008 at each location was counted to determine % winter survival. Percent 

survival across all N rates at each location was 17, 99, 79, and 100% respectively at IL, KY, NE, 

and NJ. Poor survival at Urbana, IL during the first winter required replanting in 2009 to bring 

the plots to a fully-planted status. Percent winter survival was measured again in each plot at 

each location in spring 2010.

Soil Sampling and Weather

Three 1-m deep soil cores from each plot at each location were sampled in 2008 on 17 

July in IL, 5 Aug. in KY, 14 Aug. in NE, and 3 Sept. in NJ. Each core sample was split into 5 

segments: 0-10, 10-20, 20-30, 30-50, and 50-100 cm depths. Table 3.1 summarizes selected 

variables from these soil data at each location averaged across plot and treatment. Weather data 

from stations nearby Urbana, IL; Lexington, KY; Mead, NE; and Adelphia, NJ were obtained 

from the Midwestern Regional Climate Center, a cooperative program of the Illinois State Water 

Survey and the National Climatic Data Center, or directly from the National Climatic Data 

Center. Data from each location were collected from stations nearby Urbana, IL; Lexington, 

KY; Mead, NE; and Adelphia, NJ, respectively with station name and cooperative identification 

number Urbana, IL (118740), Lexington Bluegrass AP, KY (154746), Mead 6S, NE (255362), 

Hightstown 2W, NJ (283951). Monthly weather data for 2008, 2009, and 2010 are summarized 

in Tables 3.2 and 3.3. Accumulated thermal time (aka growing-degree days) was calculated with 

a base temperature of 0°C as has been done in other studies (Miguez et al., 2008; Miguez et al., 

2009; Hastings et al., 2009).
60

Plant Measurements

Throughout the second (2009) and third (2010) growing seasons, plant growth and 

morphological measurements were collected at KY, NE, and NJ. These data were not collected 

in IL until 2010 due to 2009 replanting. Average emergence date was determined in the spring 

when approximately the first 10 plants in each plot had emerged. Date of full-headed flowering 

(R3) (Moore et al., 1991) was determined when approximately 50% of the plants in each plot 

were fully flowered. In 2009 at KY, N, and NJ, average plant height and leaf number per tiller 

were measured throughout the season. Average plant height was determined by measuring the 

height of the upper most fully expanded leaf of the tallest tiller on five randomly selected plants 

in each plot each month in 2009 and 2010. In 2009 and 2010, average tiller density (tillers m-2) 

was determined by counting the number of tillers per plant on at least five random plants in each 

plot after the end of the growing season. These sampling dates are shown in Table 3.4. In 2010 

when the tiller density measurements were collected the average number of phytomers tiller-1, 

and average tiller diameter were also determined. This was done by randomly selecting a total of 

10 representative tillers from each of five plants. Tiller diameter was determined by measuring 

each tiller at the center of the first full internode above the ground level of the tiller. Phytomer 

number was determined by recording the number of nodes on each tiller. Average tiller diameter 

and phytomer number were determined by first calculating the average within plants and then 

across all plants within a plot.

In 2009 and 2010 dry biomass yield estimates were obtained at each location after the 

end of the growing season. Harvest dates for the 2009 are shown in Table 3.4. Harvesting at all 

locations in 2009 and 2010, with exception of NE, employed the following protocol. A 

representative plant in each plot was selected, avoiding plants on the border rows of the plot. A 
61

1 m-2quadrat was centered on the middle of the plant and all standing tillers within the metersquared area were cut at ~10 cm above the ground. No ground litter was included in the sample. 

After harvesting the first plant, the 1 m-2quadrat was flipped directly to the north and the 

adjacent meter-square area (i.e. single plant) was harvested. If the adjacent sampling area 

happened to fall on edge of the plot, the quadrat was flipped to the south and that adjacent metersquare area was harvested. This process was repeated a second time by selecting a second 

representative M. x giganteus plant in each plot. This resulted in a total of 4 harvested plants, 

each representing a 1 m-2area from each M. x giganteus plot. Fresh weight was determined by 

weighing a subsample, and dry biomass (Mg ha-1) was determined by calculating the percent 

moisture of a subsample dried in the oven at 60° C for at least 48 hours. The NE plots were 

harvested using a mechanical forage plot harvester (Carter MFG Co., Inc. Brookston, IN) by 

harvesting one row of plants (10 plants) for a total area of 10 m-2in each plot. A subsample from 

each plot was collected, weighed, and dried to calculate % moisture and determine dry biomass 

yield.

Statistical Modeling and Analysis

Data from IL were not statistically analyzed with the other locations for any of the 

analyzed variables, because this location (as of 2010) possessed a mixture of two and three year 

old plants, and the stand was non-uniform. When applicable, mean values from IL were reported 

in the results to provide some information regarding this location, and these mean values were 

calculated using the MEANS procedure of SAS (SAS Institute, 2007). 

Dry biomass yield was analyzed in the MIXED procedure of SAS (SAS Institute, 2007)

while tiller density, which follow a Poisson distribution (aka count data), was analyzed in the 

GLIMMIX procedure of SAS (SAS Institute, 2007). Locations (KY, NE, and NJ) and years 
62

(2009 and 2010) were combined to create six environments: KY-2009, KY-2010, NE-2009, NE2009, NE-2010, NJ-2009, and NJ-2010. Environments, blocks, and subsamples were considered 

random effects while nitrogen rate was declared a fixed effect. The mixed model was described 

as follows

 

where dry biomass yield or tiller density ( ) depends on the 

 

random subsample of the 

 

nitrogen rate in the 

 block nested in the  

environment, having an intercept ( , and being 

influenced by the random environment ( ), random block ( ), random interaction between 

environment and nitrogen rate ( ), and fixed nitrogen rate( ) effects. The model assumes 

that , , and are independent normal random variables with expectations zero and 

respective variances σe

2

, σb(e)

2

, σeα

2

, and that the errors ( ) and subsamples ( ) have

means of zero and common variances. Environments were considered random to account for 

different weather and other environmental conditions at each site which could not be controlled. 

Significance of random effects were calculated using the COVTEST option in the MIXED and 

GLIMMIX procedures of SAS (SAS Institute, 2007). Best linear unbiased predictions of 

random effects (aka means of random effects) and their interactions were calculated using 

estimate statements with appropriate degrees of freedom and standard errors. Residuals were 

examined for normality and the assumption of common variances by inspection of residual plots.

The non-linear function used to model the increase in M. x giganteus plant height 

throughout the growing season was the logistic growth function,

 

 

 

 

 

 

[eq. 1]
63

Here, is M. x giganteus plant height (meter units) measured throughout the growing season 

and x is the day of year (DOY). Three parameters describe the shape and spread of the function: 

1) asymptote (asym) or maximum height achieved by the crop, 2) scale (scal) or the elapsed time 

between the crop achieving half and three quarters of it maximum height, and 3) inflection point 

(xmid) or DOY at which the crop achieves half of its maximum height. A nonlinear mixed 

model was used to implement the logistic growth function and investigate the effects of 

environment, N rate, and there interaction. This was accomplished by considering asym, scal, 

and xmid for each environment and each N rate as fixed components, and individual plots 

(experimental units) as random. The modeling process followed principles in Pinheiro and Bates 

(2000) and was implemented with ‘nlme’ package (Pinheiro et al., 2009) of R statistical software 

(R Core, v. 2.12.1, 2010). Residuals were checked for patterns by plotting standardized residuals 

against their fitted values. Parameter estimates and their 95% confidence intervals were obtained 

using the ‘summary’ and ‘interval’ functions of R (R Core, v. 2.12.1, 2010). Prediction plots and 

all other graphics were obtained using the ‘graphics’ and ‘lattice’ packages of R (R Core, v. 

2.12.1, 2010).

Pearson correlation coefficients and their respective P-values were calculated using the 

CORR procedure of SAS (SAS Institute, 2007) to evaluate the linear association of dry biomass 

yield, end of season plant height, tiller density, phytomers tiller-1, and tiller diameter among 

environments during the 2010 growing season. End of season plant height measurements were 

determined by selecting the last set of plant height (between Sept. and Nov.) measurements 

collected in each environment. For each variable the mean value for each plot was determined 

using the MEANS procedure of SAS (SAS Institute, 2007), resulting in 12 observations, n=12 (4 

blocks x 3 N rates) for assessing the effect of environment on these variables. Matrix scatter 
64

plots were obtained using the SGSCATTER procedure of SAS (SAS Institute, 2007) to visually 

assess correlations among environments within a growing season.

RESULTS

Winter Survival

The percent of M. x giganteus plants that survived the first winter was very dependent 

upon the site. Only 17% of the M. x giganteus plants survived the first winter at IL which 

required replanting to bring the plots to a fully-planted status during the 2009 season. The other 

three sites had adequate to excellent winter survival. KY, NE, and NJ had 99%, 79%, and 100%, 

respectively. Poor winter survival at IL was related to late transplanting in summer 2008 which 

did not occur until 24 July. In addition to being planted late, winter temperatures at IL dropped 

to -27°C in the middle of Jan. 2009. Percent winter survival at the beginning of the 2010 season 

was near 100% at each site.

Plant Height

The N rate x environment interaction was investigated and found non-significant for the

asym (P = 0.1249), xmid (P = 0.5954) and scal (P = 0.9369) parameters. The N rate main effect 

was also non-significant for the asym (P = 0.4392), xmid (P = 0.8832) and scal (P = 0.5247) 

parameters, while environment was significant for each parameter (P < .0001). Plant height 

increases throughout the growing season showed very similar patterns among different 

environments and N rates (Fig. 3.1). The estimated asym attained in the different environments 

ranged between 3 and 3.79 m, and there were significant increases in the asym in the KY and NE 

environments from 2009 to 2010 (Table 3.6). The parameters xmid and scal were quite different 

from environment to environment ranging from 143.5 to 171.5 for xmid and 15.38 to 24.23 for 
65

scal. These differences were environment dependent and appear to be related to the length of the 

growing season and weather conditions in each environment. 

Biomass Yield

The environment x N rate interaction did not contribute a significant amount of variation 

and was not significant (P = 0.2066). The main effect of N rate was not significant (P = 0.7223), 

but the environment main effect was significant (P = 0.0628). There were significant decreases 

in biomass yield at KY (P = 0.0200) and NJ (P = <.0001) from 2009 to 2010 (Table 3.7). These 

yield decreases represent 22% and 16% decreases, respectively for KY and NJ. Conversely, 

biomass yields at NE increased 75% from 2009 to 2010 (P = <.0001) (Table 3.7). Mean biomass 

yields at IL in 2009 were 1.1, 3.8, and 4 Mg ha-1for 0, 60, and 120 kg N ha-1, respectively, while 

2010 mean biomass yields at IL were 14.8, 16.1, and 16 Mg ha-1for 0, 60, and 120 kg N ha-1, 

respectively.

Tiller Density

There was no significant tiller-density response to applied N in any of the environments 

and the only significant effects was the environment main effect (P = 0.0624). There were 

significant increases in tiller densities in the KY (P = <.0001) and NE (P = <.0001) 

environments from 2009 to 2010 (Table 3.7). To put this in perspective, tiller densities in 2010 

were approximately 52% and 56% greater than in the 2009 seasons for KY, and NE, respectively 

(Table 3.7). Tiller densities were relatively constant (P = 0.7820) between the NJ-2009 and NJ2010 environments. Mean tiller densities at IL in 2010 were 37.3, 32.3, and 37.6 tillers m-2

respectively for 0, 60, and 120 kg N ha-1.
66

Correlations Among Variables

In 2010 there was a strong positive correlation between end of season plant height and 

dry biomass yield ( = 0.91) (P <.0001). There were also strong positive correlations between 

dry biomass and tiller density ( = 0.76) (P <.0001), and between plant height and tiller density 

( = 0.88) (P <.0001). There were moderate positive correlations between plant height and tiller 

diameter ( = 0.59) (P = 0.0002), and between tiller density and tiller diameter ( = 0.63) 

(P<.0001). These relationships are summarized with matrix scatter plots in Fig. 3.2 and suggest 

that higher biomass yields were primarily related to higher tiller densities and taller plant heights. 

NE stands out in the consistency of the relationships among these variables, having the highest 

biomass yields, highest tiller densities, and tallest plant heights in 2010. NJ tended to have the 

lowest amount of each of these three variables, with KY lying in between NE and NJ. Tiller 

diameter and phytomers tiller-1did not produce any noteworthy relationships to biomass yield.

DISCUSSION

The percent of M. x giganteus plants that survived the first winter was very dependent 

upon the site, and was primarily related to late planting and cold winter temperatures. The 

European literature reports that rhizomes from newly planted M. x giganteus are severely 

affected by soil temperatures less than -3.4°C (26°F), providing a lethal dose (50% kill) to the

rhizomes exposed to these temperatures (Clifton-Brown and Lewandowski, 2000). Generally, 

winterkill is primarily an issue during the establishment year. This is considering that mature 

stands of M. x giganteus have survived winters in IL with temperatures lower than -26°C (-15°F) 

(personal observations). Clifton-Brown and Lewandowski (2000) suggests that larger plants 

having rhizomes with low moisture content that initiate dormancy on their own before the first 

fall frost will tend to overwinter better than M. x giganteus plants that are shorter, have high 
67

rhizome moisture content and that do not begin dormancy until the first fall frost occurs. To 

promote overwintering capability, M. x giganteus should be planted in mid-to-late spring, 

providing ample time for the crop to develop and prepare for winter by developing larger 

rhizome biomass.

M. x giganteus is impressive in its ability to begin growth early in the growing season and 

it is interesting to note, that approximately half of the crops plant height is achieved by late May 

in KY and by mid June in NE (Table 3.6). This implies that this crop may reach 1.5 to 2 m by 

early June. This early growth habit, allows the crop to close its canopy relatively early in the 

growing season, allowing it to intercept more photosynthetic active radiation. M. x giganteus

has been shown to intercept about 95% of photosynthetic active radiation between June and the 

later end of the growing season, when senescence begins (Heaton et al., 2008). M. x giganteus

achieves greater amounts of seasonal canopy-level photosynthesis than some other C4

photosynthetic grasses such as perennial switchgrass (Panicum virgatum L.) (Dohleman et al.,

2009) and maize (Zea mays) (Dohleman and Long, 2009). In addition to being able to develop a 

leaf canopy early in the season it also has the ability to remain vegetative late into the season, 

when grown in northern environments, resulting in a long canopy duration (Dohleman and Long, 

2009). The sites selected for this study, generally allowed the crop to remain green and 

vegetative until at least early September (Table 3.5). The ability of this crop to remain 

vegetative late in the season provides time for the crop to intercept more light and potentially 

produce more biomass. This generally should be true as long the crop is not grown too far north 

where longer day lengths in late summer and early fall could potentially keep the crop from 

flowering and properly entering dormancy. In environments where winter kill is a concern, there 
68

should be ample time between flowering and the first killing frost, allowing carbohydrates to 

translocate to storage organs and build up of these carbohydrates for winter survival.

The dry biomass yields at each location in 2009 were slightly higher than other second 

season biomass yields that have been achieved in the USA. Second season biomass yields in this 

study were 17, 15.6, and 13.5 Mg ha-1in KY, NE, and NJ, respectively. These values are higher 

than second season mean biomass yields in a study in Kansas, USA that ranged from 11.8 Mg 

ha-1to 13.7 Mg ha-1depending on the site (Propheter and Staggenborg, 2010). The growing 

conditions were adequate for growth in each environment in 2009. This was especially true in 

KY which had more growing-season precipitation (April through September) (863 mm) than 

either NE (527 mm) or NJ (829 mm) in 2009 (Table 3.2). These precipitation levels are reflected 

in the yields that were attained in 2009, except for NJ where the yields were lowest; this is most 

likely due to the shallow, sandy soils at that site (Table 3.1). Tiller densities at each site in 2009 

were similar at 38 tillers m-2in KY, 44.5tillers m-2in NE, and 43.8 tillers m-2in NJ. It was 

expected that tiller densities and biomass yields would have increased from 2009 to 2010 at each 

site (Miguez et al., 2008), but this was not the case.

Dry biomass yield were surprisingly low at KY in 2010 and this was due to abnormal 

weather conditions that resulted in a highly stressed crop. The crop emerged on 2 April (Table 

3.5), and a late frost on 19 April resulted in some frost damage. The effects of this frost damage 

were not quantified, but it is likely that some of the actively growing tillers were either killed or 

stunted. Farrell et al. (2006) showed that newly emerging shoots exposed to freezing 

temperatures can have a negative impact on M. x giganteus biomass yields. Early season 

precipitation was much higher than normal, with 253 mm precipitation in May, 132 mm more 

than the long term average. The latter end of the growing season was very dry, with 
69

precipitation levels of 15 mm in both Aug. and Sept. The normal precipitation levels for these 

months are 96 mm and 79 mm, respectively. Limited precipitation in Aug. and Sept. was 

coupled with hot summer temperatures resulting in 44 days with the temperature greater than or 

equal to 32.2°C between May and Sept. Generally, high summer temperatures should not have a 

large negative impact on this C4 crop, but when combined with low precipitation, it appears to 

generate highly stressful growing conditions. The combination of conditions present at KY in 

2010 caused the M. x giganteus plants to go dormant earlier than normal while the crop was still 

in a vegetative growth stage. This resulted in the M. x giganteus crop not flowering in 2010 

(Table 3.5).

Similar weather patterns were observed in NJ in 2010. New Jersey weather was very 

warm in 2010 accumulating 4601 thermal heating units compared to 3459 heating units in 2009. 

In 2010, there were 49 days between May and Sept. when the temperature was greater than or 

equal to 32.2°C (90°F). High temperatures were combined with less than normal precipitation 

(399 mm) between April and September compared to the average for this period (670 mm). 

Growing-season precipitation has been shown to be extremely important for M. x giganteus to 

achieve its biomass yield potential (Heaton et al., 2004). At one mature M. x giganteus site in 

England, variables relating available water capacity and soil moisture to biomass yield during the 

growing season accounted for 70% of annual biomass yield variation (Richter et al., 2008). 

Areas of low precipitation or prolonged periods of drought are not suitable for growing M. x 

giganteus, as it lacks drought tolerance or the capacity to survive under low precipitation 

environments, and will begin to senesce under water stressed conditions (Clifton-Brown et al.,

2002). In NJ in 2010, not all of the plots reached full maturity with only half of the plots 

flowering. Those that flowered were the plots fertilized with 0 kg N ha-1or 60 kg N ha-1. There 
70

was limited flowering in the 60 kg ha-1plots and none of the 120 kg N ha-1plots flowered. The 

NJ plots that did flower did so on 30 Oct., and the season’s first fall frost on 1 Nov., limited 

flowering of any remaining plants. Similar results have reported that nitrogen-fertilized plots of 

M. x giganteus retain their leaves and potentially remain vegetative longer than unfertilized plots 

(Himken et al., 1997); however, a difference in the yields of these plots was not observed once 

leaf senescence was complete and the plots were harvested in the winter. Lower yields at NJ in 

2010 were was due to several reasons including weather conditions that were both warmer and 

drier than normal. Also, the standing residue left behind following the winter harvest from the 

2009 season was not mowed until early April 2010, around the time the crop was beginning to 

green up. This may have damaged some emerging shoots and consequently affected the 2010 

biomass yields. The shallow and sandy soils at that site may have also contributed to the low 

yields. In particular, the bedrock layer that is present between 50 and 80 cm is possibly 

impeding M. x giganteus root development and restricting the plant’s ability to obtain water at 

deep soil depths. In a mature stand of M. x giganteus in Germany, roots have been shown to 

reach down to 250 cm in the soil, with almost half of the roots deeper than 90 cm (Neukirchen et 

al., 1999). 

In 2010, NE growing conditions were favorable for crop growth, and this is reflected in 

the high biomass yields which were highly correlated with end of season plant height (Table 3.7, 

Fig. 3.2). Similar results relating biomass to plant height have been reported in production in 

central Italy (Angelini et al., 2009). Between Apr. and Sept. there was 814 mm of precipitation, 

295 mm greater than the normal rainfall for this period at the NE site in 2010. This amount of 

rainfall was greater than any of the other sites in 2010, which received 570, 613, and 399 mm 

respectively in IL, KY, and NJ. Additionally, there were 25 days during the growing season at 
71

NE when the temperature was greater than or equal to 32.2°C (90°F). This combination of warm 

temperatures and adequate precipitation levels spread throughout the growing season promoted 

an ideal set of growing conditions. Optimal growing temperatures for M. x giganteus are 

between 30 and 35°C (Naidu et al., 2003).

Even though there was no significant effect of applied N on any of the measured 

variables in any of the environments, it does not suggest, however that M. x giganteus does not 

require N fertilizer. As the literature suggests, N response varies from site-to-site. To fully 

understand the N needs of M. x giganteus, various aspects of the crop and the site where it is 

being grown require consideration. Since M. x giganteus has the ability to recycle its nutrients 

from year to year, much of the crop’s nutrient needs are likely met on an annual basis without 

annual applications of N fertilizer provided, harvesting takes place in the winter after nutrients 

from above-ground plant parts have been recycled via translocation from shoots to rhizomes, 

fallen leaves, and leaching from stems (Cadoux et al., 2011). In a review, Cadoux et al. (2011) 

reported that three year old stands of M. x giganteus harvested during the winter remove 

approximately 4.9 g N kg-1dry matter, and maximum N fertilization rates should be 49, 73.5, 

and 98 kg N ha-1, respectively, for harvestable dry biomass yields of 10, 15, and 20 Mg ha-1. 

This study also recommended that fertilizer should not be applied during the first two years after 

M. x giganteus is planted, unless it is planted in poor soils. This recommendation is supported 

by a recent meta-analysis reporting that there was little response to N fertilizer during the 

establishment years (years 1-3) of M. x giganteus but once the crop reached maturity, a response 

to N fertilizer was detected (Miguez et al., 2008). A lack of N fertilizer response is doubly 

supported when M. x giganteus is planted in locations where soil N is abundant, or where high 

levels of soil organic matter can meet the N needs of the crop through N mineralization from soil 
72

organic matter. At this reporting, our study is still in its ‘yield building’ or establishment years 

(Clifton-Brown et al., 2007), however, a response to N may occur in future years of production at 

some locations as the biomass yields more or less stabilize. It can be anticipated that if any site 

shows a response to N in the future, it will occur at the IL or NJ sites where soil texture is coarse 

and there are relatively low amounts of soil organic matter. With rooting depths reaching down 

to 250 cm in mature stands, roots can explore large areas of soil for nutrients (Neukirchen et al.,

1999). An extensive root system tends to limit the amount of N that is leached from the soil 

profile. It is also important to consider how much N is being removed from the system at harvest 

and how much is being recycled back into the system. Christian et al. (1997), reported that of 

the 117 kg N ha-1that the crop acquired from the soil during the growing season, about 20% 

came from the labeled N source when 60 kg N ha-1of labeled N was applied to M. x giganteus

during the second season of growth. The authors suspected that the majority of the actual N 

taken up by the crop came from mineralized soil organic matter. 

CONCLUSIONS

This study increases our understanding of how different environments impact M. x 

giganteus morphology, growth, and biomass yield. Not surprisingly, increases in biomass yield 

from season two to season three rely on good growing conditions. Significant biomass yield 

decreases from 2009 (season 2) to 2010 (season 3) were related to a late spring frost and 

excessively wet spring, followed by a hot and excessively dry late summer in KY, and hot, dry 

weather, and poor soil conditions in NJ. Significant increases in dry biomass yields between 

season two and season three are highly correlated with taller plants and greater tiller densities, 

which are a function of adequate precipitation and warm summer temperatures, as was the case 

for NE in 2010. Also of importance, nitrogen fertilization had no significant effects on M. x 
73

giganteus biomass in season two or three at any site. As the crop matures and becomes fully 

productive, this experiment will shed important light on the capacity of M. x giganteus to 

provide stable and reliable biomass yields at these locations. 

Acknowledgements

The authors acknowledge the Sun Grant/U.S. Department of Energy Regional Biomass 

Feedstock Partnership for funding this study, as well as the Energy Biosciences Institute Energy 

Farm at the University of Illinois Urbana-Champaign for providing land and labor support for 

this study.

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77

TABLES

Table 3.1. Selected soil variables from each site sampled during summer 2008 at five soil depths (0-10, 10-20, 23-30, 30-50, 

and 50-100 cm). 

Total (%) -----Extractable (mg/kg)------

Location depth, 

cm

% 

sand

% 

silt

% 

clay

pH CEC, cmolc

kg–1

% SOM C N P K Ca Mg S BD, g 

cm-3

Urbana, IL 0-10 55 30 16 5.7 10.6 1.9 1.14 0.11 39.1 110 1390 154 15.8 1.52

10-20 54 30 15 5.9 10.7 1.9 1.11 0.11 47.1 144 1613 154 17.1 1.69

20-30 52 32 16 6.0 10.9 1.9 1.08 0.11 39.5 130 1686 177 17.4 1.66

30-50 36 43 21 6.0 12.0 1.6 0.75 0.08 11.3 75 1722 325 13.4 1.59

50-100 11 53 35 6.5 20.9 1.2 0.41 0.05 1.4 126 2659 840 11.4 1.64

Lexington, KY 0-10 9 64 27 5.1 18.6 4.7 2.49 0.31 322.2 229 1860 214 45.5 1.38

10-20 9 63 28 5.8 16.2 3.2 1.64 0.21 302.8 117 2122 174 25.2 1.56

20-30 8 60 32 5.9 16.0 2.4 1.08 0.15 321.7 91 2145 144 16.3 1.57

30-50 10 52 38 6.0 17.8 1.8 0.66 0.10 383.4 92 2405 133 12.5 1.71

50-100 14 38 48 5.9 25.6 1.8 0.46 0.09 391.8 106 3453 146 17.3 1.83

Mead, NE 0-10 4 59 36 6.1 22.4 5.1 2.99 0.33 108.3 667 3053 610 19.6 1.23

10-20 4 58 39 6.7 22.6 3.6 1.98 0.24 74.7 672 3082 634 16.2 1.39

20-30 4 55 42 6.7 23.3 3.2 1.70 0.20 60.3 614 3098 715 14.9 1.40

30-50 3 54 43 6.9 23.8 2.5 1.18 0.14 28.4 575 3006 834 13.7 1.45

50-100 4 56 40 7.0 23.2 1.4 0.53 0.07 24.9 414 2861 879 11.6 1.53

Adelphia, NJ 0-10 52 35 13 5.3 9.5 2.1 1.23 0.11 219.2 119 800 154 19.3 1.34

10-20 53 35 12 5.5 9.6 2.2 1.24 0.12 220.5 95 874 158 18.1 1.52

20-30 53 35 13 5.5 9.1 2.0 1.14 0.11 202.3 92 816 155 17.4 1.57

30-50 55 30 15 5.5 5.8 1.1 0.48 0.04 43.4 69 627 177 15.4 1.68

50-100 70 15 16 5.4 5.0 0.8 0.24 0.03 14.7 70 643 141 27.9 -

*

Abbreviations are as follows: cation exchange capacity (CEC), soil organic matter (SOM), and bulk density (BD). 

* a restrictive soil feature ranging between 50 and 80 cm precluded the calculation of bulk density at this NJ site. 
78

Table 3.2. Total monthly precipitation (mm) at each location during 2008, 

2009, and 2010, and their 30-yr normal averages.

------------------IL------------------ -----------------KY-----------------

Month 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal

Jan 59 17 31 48 112 110 76 85

Feb 151 43 41 51 146 65 41 83

Mar 72 67 74 82 160 61 29 112

Apr 76 176 53 93 150 121 59 93

May 154 145 87 122 112 153 253 121

Jun 163 112 212 107 91 132 117 116

Jul 200 160 95 119 87 192 154 122

Aug 20 143 42 111 55 115 15 96

Sep 207 20 81 82 36 150 15 79

Oct 75 223 28 71 39 147 31 69

Nov 33 100 98 88 64 24 113 87

Dec 124 96 65 70 153 102 63 102

Annual 1336 1302 906 1043 1205 1372 966 1166

------------------NE----------------- ------------------NJ-----------------

Month 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal

Jan 6 7 23 12 69 71 67 95

Feb 10 12 17 13 110 15 110 70

Mar 17 8 41 47 83 47 229 100

Apr 118 41 102 70 62 99 67 100

May 151 30 68 106 116 112 82 112

Jun 251 165 249 101 107 187 78 100

Jul 95 67 183 84 89 159 75 126

Aug 26 185 64 85 39 172 20 123

Sep 110 39 148 73 178 100 77 109

Oct 129 110 6 55 90 119 82 87

Nov 45 0 0 40 84 64 50 93

Dec 30 67 0 18 151 166 74 95

Annual 987 732 902 704 1177 1311 1012 1211
79

Table 3.3. Average minimum and maximum air temperature (°C) at each location (IL, KY, NE, and NJ) during 2008, 2009, and 2010, and their 30-yr 

normal averages.

----------------------------------------IL--------------------------------------- --------------------------------------KY-------------------------------------

-------average minimum (°C)------- -------average maximum (°C)------- -------average minimum (°C)------- -------average maximum (°C)-------

Month 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal

Jan -8.6 -12.3 -10.1 -8.8 1.8 -2.4 -3.4 0.0 -4.4 -6.5 -5.8 -4.4 4.7 2.6 1.7 4.4

Feb -7.9 -5.7 -8.0 -6.1 0.7 4.7 -0.4 3.1 -2.3 -2.1 -5.6 -2.4 6.2 8.5 2.1 7.3

Mar -1.1 0.1 1.1 -0.9 8.5 12.6 11.9 9.7 1.2 3.4 2.9 2.2 12.3 14.3 13.4 12.9

Apr 4.7 5.0 7.6 4.4 16.6 16.3 21.3 16.8 6.8 7.4 8.5 6.7 18.2 18.7 22.0 18.4

May 8.9 11.4 12.5 10.6 20.4 23.3 23.7 23.1 10.5 13.1 14.4 12.0 22.0 23.1 24.3 23.3

Jun 17.2 18.1 18.6 15.8 28.6 29.2 29.0 28.1 17.4 17.9 19.5 16.8 29.0 28.3 30.1 27.9

Jul 17.6 16.0 19.4 18.0 28.8 26.2 30.5 29.6 18.2 17.6 20.3 19.1 30.2 26.8 30.8 29.9

Aug 16.5 15.8 18.8 16.9 28.0 26.9 31.3 28.4 17.3 17.9 19.4 18.3 30.0 27.9 31.8 29.2

Sep 14.1 13.9 12.9 12.4 25.3 24.7 26.3 25.3 15.5 15.6 14.2 14.4 28.4 24.9 28.8 25.6

Oct 6.1 5.3 6.2 6.1 19.2 14.4 20.8 18.4 7.4 6.9 7.4 8.0 20.6 16.6 22.2 19.4

Nov -0.6 2.7 -0.7 0.3 8.9 13.1 12.1 9.7 0.9 3.7 2.1 2.9 11.2 14.1 14.4 12.5

Dec -7.8 -6.1 -8.8 -5.6 1.3 1.9 -1.4 2.7 -3.3 -2.4 -5.8 -2.0 7.6 5.8 0.8 6.8

----------------------------------------NE---------------------------------------- ---------------------------------------NJ--------------------------------------

-------average minimum (°C)------- -------average maximum (°C)------- -------average minimum (°C)------- -------average maximum (°C)-------

Month 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal 2008 2009 2010

30-year 

normal

Jan -12.9 -12.4 -13.4 -12.4 -1.6 0.4 -5.6 -0.7 -3.9 -7.6 -4.9 -5.8 6.4 1.3 4.1 3.7

Feb -11.6 -8.4 -11.7 -9.1 -0.4 6.5 -2.7 2.8 -3.4 -4.8 -5.0 -4.8 7.4 7.2 3.4 5.1

Mar -5.0 -4.2 -1.8 -3.0 9.6 10.2 8.8 9.4 -0.2 -1.1 2.4 -0.4 11.2 10.4 13.4 10.1

Apr 0.7 1.1 5.2 3.4 13.4 16.1 20.3 16.7 5.7 5.4 6.3 4.1 18.1 17.4 20.1 16.1

May 7.9 9.1 8.9 9.8 21.5 23.7 21.4 22.8 8.6 10.6 10.8 9.4 20.8 22.1 24.9 22.0

Jun 14.9 15.0 15.9 15.3 27.8 27.3 28.4 28.6 16.1 14.7 17.1 14.4 29.4 24.9 30.0 26.8

Jul 17.3 14.8 18.4 17.9 29.9 27.2 29.6 30.8 18.4 15.7 19.8 17.3 31.0 28.8 32.3 29.4

Aug 15.2 14.3 17.4 16.4 29.4 27.5 31.1 29.3 14.4 17.6 18.4 16.4 28.9 29.5 29.9 28.4

Sep 10.4 10.3 11.8 11.1 24.6 24.1 25.5 25.3 13.4 11.7 14.1 12.2 25.6 23.7 27.8 24.6

Oct 4.2 1.8 3.8 4.1 18.2 12.4 21.7 18.5 3.7 5.4 7.4 5.7 18.6 17.0 19.5 18.4

Nov -2.2 -0.7 . -2.9 10.1 13.3 . 8.6 1.3 4.8 0.9 1.4 10.9 14.4 13.3 12.3

Dec -12.3 -12.5 . -9.3 -0.3 -1.9 . 1.3 -3.0 -3.1 -5.4 -2.9 7.8 6.3 4.1 6.4
80

Table 3.4. Sampling dates from the 2009 and 2010 growing seasons for plant height, tiller density, and biomass yield 

harvest at each location (IL, KY, NE, and NJ).

-----------------------------------------------------------2009 growing season--------------------------------------------------------

location plant height

----------------------------------measurements----------------------------------

tiller density 

measurements

biomass yield 

harvest

IL Jan. 2010†

KY 24 Apr., 18 May, 19 June, 27 July, 4 Sept. 4 Sept. 16 Mar. 2010†

NE 10 May, 10 June, 15 July, 17 Aug., 15 Sept. 15 Sept. 1 April 2010†

NJ 15 May, 17 June, 22 July, 20 Aug.,18 Sept., 23 Oct. 23 Oct. 16 Dec. 2009

---------------------------------------------------------2010 growing season----------------------------------------------------------

location plant height

----------------------------------measurements----------------------------------

tiller density 

measurements

biomass yield 

harvest

IL 29 Nov. 2010 29 Nov. 2010

KY 9 Apr., 10 May, 11 June, 17 July, 9 Aug., 14 Sept., 12 Oct. 11 Nov 11 Nov. 2010

NE 28 Apr., 26 May, 28 June, 28 July, 24 Aug., 27 Sept., 26 Oct., 22 Nov. 2 Dec 2 Dec 2010

NJ 17 May, 17 June, 15 July, 18 Aug., 24 Sept., 13 Oct., 2 Nov. 9 Dec 9 Dec 2010

†Harvesting for the 2009 growing season in IL, KY, and NJ actually occurred early in 2010 before the start of the 2010 

growing season.
81

Table 3.5. Season length, accumulated thermal time, average emergence date, and flowering date(s) for each 

growing season and location (KY, NE, and NJ).

location season length†

accumulated 

thermal time‡emergence date§

flowering

date(s)

¶

-----------------------------------------------------------2009 growing season------------------------------------------------------

KY 193 days (8 Apr. – 18 Oct.) 3783 31 Mar. 18 Sept.

NE 172 days (15 Apr. – 4 Oct.) 3196 26 Apr. – 1 May 23 Sept.

NJ 185 days (17 Apr. – 19 Oct.) 3459 27 Apr. ~25-30 Sept.

-----------------------------------------------------------2010 growing season------------------------------------------------------

IL 196 days (10 Apr. – 23 Oct.) 3911 10 Apr. 1 Oct.

KY 217 days (27 Mar. – 30 Oct.) 4531 2 Apr. did not flower 

NE 167 days (19 Apr. – 3 Oct.) 3402 2 week Apr. 27 Sept.

NJ 218 days (28 Mar. – 1 Nov.) 4601 11 Apr. 30 Oct.#

†

season lengths were calculated as the number of days between the last frost in the spring to the first frost in the fall. 

One exception is in KY in 2010, where a late frost on 19 Apr. was not used as the beginning of the growing season 

since there had already been two-to-three weeks of above-freezing weather since the previous frost on 27 March. In 

this case, 27 Mar. was marked as the last frost in the spring.

‡ accumulated thermal time was calculated with a base temperature of 0°C, by determining the average of the 

minimum (when greater than 0°C) and maximum daily (no limit) temperatures, and summing these values across 

time. In calculating accumulated thermal time for individual days, if the average temperature for that day did not 

exceed 0°C, no TT was accumulated

§ emergence date was determined in the spring when approximately the first 10 plants in each plot had emerged. 

¶

date of full-headed flowering was determined when approximately 50% of the plants in each plot were fully 

flowered.

#

only plots that flowered at Adelphia, NJ in 2010 were those plots applied with 0 kg N ha-1and some that were 

applied with 60 kg N ha-1. Those that did flower, flowered on 30 Oct. First fall frost was 1 Nov. 
82

Table 3.6. 95% confidence intervals (upper and lower) of the parameters estimates of the logistic growth function 

for each environment (KY-2009, KY-2010, NE-2009, NE-2010, NJ-2009, and NJ-2010) fit for M. x giganteus plant 

height.

Parameter environment lower estimate upper

asym KY-2009 2.99 3.08 3.17

KY-2010 3.19 3.39 3.59

NE-2009 2.62 2.85 3.07

NE-2010 3.59 3.79 3.99

NJ-2009 3.17 3.37 3.57

NJ-2010 2.80 3.00 3.20

xmid KY-2009 149.1 151.5 153.8

KY-2010 138.3 143.5 148.7

NE-2009 165.4 171.5 177.6

NE-2010 156.7 161.7 166.8

NJ-2009 155.1 160.2 165.4

NJ-2010 141.8 146.8 151.9

scal KY-2009 19.71 21.75 23.79

KY-2010 16.42 20.86 25.30

NE-2009 19.08 24.23 29.37

NE-2010 17.53 21.84 26.15

NJ-2009 15.31 19.82 24.33

NJ-2010 10.96 15.38 19.81

asym = maximum height (m), xmid = day of year at which crop achieves half of its maximum height, scal = time (in 

terms of days) between half and three quarters maximum height.
83

Table 3.7. M. x giganteus dry biomass yield (Mg ha-1) and tiller density (number tillers 

m

-2

) estimated for each environment at each of three N rates (kg N ha-1) and their means 

averaged across N rates.

-----N rate, kg N ha-1------

Environment 0 60 120 mean†

p-value‡

---------------dry biomass, Mg ha-1----------------

KY-2009 16.7§17.5 16.9 17 0.0200 KY-2010 12.8 13.2 13.6 13.2

NE-2009 15.7 15.9 15.2 15.6 <.0001 NE-2010 27 27.8 27.4 27.3

NJ-2009 12.2 14.3 13.8 13.5 0.1000 NJ-2010 11.6 11.7 10.7 11.4

------------------number tillers m-2------------------

KY-2009 38.8 37.4 38.5 38.2 <.0001 KY-2010 58.8 56.8 58.4 58

NE-2009 45.2 43.6 44.8 44.6 <.0001 NE-2010 70.4 67.9 69.8 69.3

NJ-2009 44.7 43.2 44.3 44.1 0.7820

NJ-2010 44.0 42.4 43.6 43.3

† mean values are averaged across N rate.

‡ P values for contrast statements comparing mean environment values within a location 

(KY-2009 vs. KY-2010, NE-2009 vs. NE-2010, and NJ-2009 vs. NJ-2010).

§

no contrasts among N rates were made because the environment x N rate and N rate 

effects were non-significant for both dry biomass and tiller density.
84

FIGURES

Figure 3.1. M. x giganteus plant height (m) plotted against day of year (DOY) and fit with logistic growth functions 

for each N rate (0, 60, 120 kg N ha-1) in each of six environments (KY-2009, KY-2010, NE-2009, NE-2009, NE2010, NJ-2009, and NJ-2010).
85

Figure 3.2. Matrix scatter plots showing relationships between M. x giganteus dry biomass yield (Mg ha-1) end of 

season plant height (m), tiller density (number tillers m-2), average number of phytomers tiller-1, and average tiller 

diameter (cm) grouped by different 2010 environments: KY-2010, NE-2010, and NJ-2010.
86

CHAPTER 4

Management of Forage and Energy Sorghum for Biomass Production in Illinois

ABSTRACT

No research has been published in IL evaluating forage and energy sorghum as biomass 

feedstock crops. This study was conducted to evaluate the growth and biomass potential of these 

sorghum types in different IL environment under different nitrogen (N) rates. Two forage 

sorghum and two energy sorghum hybrids each were evaluated in 2009 in one environment 

(Urbana 2009) under four N rates (0, 50, 100, and 150 kg N ha-1), and in 2010 in three 

environments (Urbana 2010, Dixon Springs 2010, and Perry 2010) under five N rates (0, 56, 112, 

168, and 224 kg N ha-1). Measurements of biomass yield, plant height, leaf development, and 

leaf area index (LAI) were collected to evaluate the effects of environment, N rate, sorghum 

type, and sorghum hybrid. Environment had a major impact on plant growth and total biomass 

yields. Forage sorghum harvested twice annually (summer and fall) yielded less than energy 

sorghum harvested once in the fall in both 2009 (P < 0.0114) and 2010 (P < 0.0734). Total 

energy and forage sorghum biomass yields averaged across all 2010 environments were 30.1 Mg 

DM ha-1and 19.2 Mg DM ha-1, respectively. Biomass yield, LAI, and plant height response to N 

rate were observed up to 150 kg N ha-1in 2009 and up to at least 168 kg N ha-1in 2010. Energy 

sorghum reached plant heights of 3.7 m, 4.1 m, and 3.8 m in the Dixon Springs 2010, Perry 

2010, and Urbana 2010 environments, respectively. Leaf development was linearly related to 

GDD and in the warmest environments the average number of fully expanded leaves reached 28 

on the energy sorghum hybrids. Photoperiod-sensitive energy and forage sorghum hybrids 

possess great potential in IL as biomass crops because of their ability to remain vegetative until 

late Sept., and therefore produce large amounts of biomass. 
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INTRODUCTION

The need to produce large quantity of bioenergy feedstock has become an increasingly 

important priority in the US as energy prices continue to increase and foreign oil and gas become 

more volatile (Perlack et al., 2005; Asif and Muneer, 2007; U.S. Chamber of Commerce, 2010). 

Continued research efforts and the scaling up of a plant and biomass based energy industry is 

necessary to meet the US’s vision of replacing 30% of current petroleum consumption by 2030 

with biofuels (Perlack et al., 2005). These efforts will also help to meet the government mandate 

of producing 136 billion liters of renewable fuels by 2022, of which 60 billion liters must come 

from cellulosic sources (Energy Independence and Security Act of 2007). Forage (Sorghum 

bicolor L.) and biomass (referred to as energy sorghum) sorghums are sorghum types that can 

help meet this need because these grasses produce large amounts of structural carbohydrates 

(cellulosic feedstock) in their biomass (McBee and Miller, 1990; Rooney et al., 2007; Hartley et 

al., 2009). Sorghum is a multipurpose C4 photosynthetic, annual grass that has been grown as a 

grain crop for food and feed and as a forage crop. More recently, forage and energy sorghum 

hybrids have been developed and identified as promising biomass crops. These sorghum types 

possess potential because of potentially high yields, efficient water-use efficiency, drought 

tolerance, seed reproduction, wide adaptability, and potential for genetic improvement (Rooney 

et al., 2007; Miller and McBee, 1993). 

Forage sorghum includes sorghum x sudangrass and brown midrib hybrids that have been 

used primarily in the livestock industry (McCollum et al., 2005; Beyaert and Roy, 2005). 

Recent research and hybrid improvements, however, suggest that brown midrib forage sorghum 

and certain photoperiod-sensitive forage sorghum hybrids possess great biomass yield potential 

(Rooney et al., 2007; Venuto and Kindiger, 2008; Hallam et al., 2001). Because brown midrib 
88

hybrids have relatively low levels of lignin, the cellulosic ethanol conversion process may be 

more efficient than with other sorghum hybrids containing higher levels of lignin (Propheter et 

al., 2010). Photoperiod-sensitive forage sorghum hybrids (as well as energy sorghums) will 

continue vegetative growth until day lengths are 12 hrs 20 min (Rooney and Aydin, 1999; 

McCollum et al., 2005). This ability to remain vegetative late into the growing season is also an 

advantage in dryland or rainfed agriculture because sorghums’ tolerance to drought is greater 

while growing vegetatively (Rooney et al., 2007) than reproductively when significant stress can 

occur (Prasad et al., 2008). Forage sorghum hybrids also have the ability to ‘rattoon’ or regrow 

after being harvested and can produce multiple cuttings in one season (Beyaert and Roy, 2005; 

Ketterings et al., 2007). Photoperiod-sensitive forage sorghum hybrids have reached mean dry 

biomass yields of 24.0 Mg dry matter (DM) ha-1 with only one cutting in Bushland, TX 

(McCollum et al., 2005), and 31.9 Mg DM ha-1in College Station, TX (Miller and McBee, 

1993). In a study in El Reno, OK a double cutting (Aug. and Nov.) of several forage sorghum 

hybrids yielded an average of 25.5 Mg DM ha-1dry biomass whereas a single cutting of the same 

hybrids in Sept. yielded 27.0 Mg DM ha-1 (Venuto and Kindiger, 2008). In this study the 

highest yield was achieved from a single Sept. harvest of a photoperiod-sensitive forage sorghum 

hybrid that produced 40.3 Mg DM ha-1. However, multi-cut sorghum biomass total yields are 

usually greater than single cut sorghum biomass yields in tropical and subtropical regions 

(Rooney et al., 2007). 

Energy sorghum hybrids have been developed from photoperiod-insensitive sorghum lines, 

are photoperiod-sensitive and similar to forage sorghum hybrids, but have been designed to be 

harvested once per year and possess even greater biomass yield potential than forage sorghum 

hybrids (Rooney and Aydin, 1999; Rooney et al., 2007; Sorghum Management Guide, 2010). 
89

These energy sorghum hybrids contain large amounts of structural carbohydrates such as 

cellulose and hemicellulose in the leaves and stalks that can be converted to energy via 

combustion or into ethanol using cellulosic ethanol conversion methods (McBee and Miller, 

1990; Hartley et al., 2009). Energy sorghum hybrids are photoperiod-sensitive, remain 

vegetative late into the growing season, have been shown to grow for more than 200 days in TX, 

and may grow 6 m tall(Sorghum Management Guide, 2010), while forage sorghum hybrids may 

reach heights greater than 4.3 m (Marsalis, 2011). An advantage of growing these photoperiodsensitive forage and energy sorghum hybrids in more northern environments such as the upper 

Midwest, allows vegetative growth to continue until late in the growing season. Panicle and 

flower initiation triggers the end of vegetative biomass accumulation. As long as vegetative 

growth continues, biomass will continue to accumulate, which is ideal for a bioenergy crop that 

will be used as dry biomass feedstock (Katrin et al., 2009). No literature coming from IL has 

been published on forage and energy sorghum produced for biomass feedstock purposes. 

However, it is expected that when photoperiod-sensitive forage and energy sorghum hybrids are 

grown in IL, vegetative biomass accumulation will continue until mid or late Sept. because 

before this time period day lengths are longer than 12 hrs 20 min and the average date of the first 

fall frost ranges from 21 Oct. in the southwestern region to 7 Oct. in the northern region (IL State 

Water Survey, 2011, http://www.isws.illinois.edu/atmos/statecli/Frost/first_fall_frost.htm). 

There have been studies that have investigated the N response of sorghum when grown for 

hay and silage, but information is scant regarding the response of forage and energy sorghum to 

N fertilizer when grown as a biomass feedstock crop. Studies evaluating N rates for sorghum 

have produced variable results, and as expected, are due in great part to site-to-site variation and 

different management practices that have been implemented. A management guide for forage 
90

sorghum suggests that in irrigated environments with high yield potential, N rates as high as 269 

kg N ha-1 may be needed, but under dryland conditions little to no N fertilizer may be required 

(Marsalis, 2011). In ON, Canada sorghum x sudangrass cut three times in July, Aug., and Sept. 

for hay reached maximum yields of 5.95 Mg DM ha-1 with an N application rate of 125 kg N ha1

, although economical optimum N rates were calculated at between 83 kg N ha-1and 107 kg N 

ha-1depending on the prices of N fertilizer and hay (Beyaert and Roy, 2005). Sweet sorghum 

and forage sorghum reached near maximum yields at 140 kg N ha-1yr-1 when harvested once in 

Sept. and between 1988 and 1992 where forage sorghum mean biomass yields ranged from 14.6 

to 16.7 Mg DM ha-1at Ames, IA, and 12.6 to 21.8 Mg DM ha-1at Chariton, IA (Hallam et al., 

2001). In a study in NY, economic optimum N rates based upon a quadratic plus plateau model 

ranged between 137 and 192 kg ha-1per cutting in four out of six site-years for brown midrib 

sorghum x sudangrass cut two times per year in July or Aug. and then again in Sept. (Ketterings 

et al., 2007). The other two sites had lower economical optimum N rates; 120 kg N ha-1 where a 

manure application in addition to the synthetic N fertilizer treatments had been applied, and < 56 

kg N ha-1 when following a spring plow down of a long term grass and legume stand. In a KS 

study that evaluated several annual and perennial crops for biofuel feedstock production, 

photoperiod-sensitive sorghum reached dry biomass yield of 26.8 Mg DM ha-1 when fertilized 

with 180 kg N ha-1, while three other forage sorghum hybrids reached mean biomass yields 

ranging from 14.8 Mg DM ha-1to 20.7 Mg DM ha-1also with 180 kg N ha-1(Propheter et al., 

2010). Under limited irrigation in Lubbock, TX the optimum N rate for ethanol production from 

sweet sorghum and photoperiod-sensitive sorghum was 108 kg N ha-1but the economical 

optimum N rate ranged between 59 and 101 kg N ha-1(Tamang et al., 2011). 
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The overall objective of this study was to evaluate forage and energy sorghum grown for 

biomass feedstock production in IL. To meet this objective, two forage sorghum hybrids, and 

two energy sorghum hybrids were grown in four IL environments under different nitrogen rates. 

Measurements of biomass yield, plant height, leaf development, and leaf area index (LAI) were 

collected and the effects of environment, N rate, sorghum type, and sorghum hybrids were 

evaluated.

MATERIALS AND METHODS

This field study was conducted in Urbana, IL (40°04’ N, 88°12’W) in 2009 and 2010 and 

in Dixon Springs, IL (37°27’ N, 88°43’ W), and Perry, IL (39°48’ N, 90°49’W) in 2010, 

resulting in four environments in which the effects of N on the growth and yield of sorghum 

grown for biomass were evaluated. These sites are referred to as Urbana 2009, Urbana 2010, 

Dixon Springs 2010, and Perry 2010. The soils were a Flanagan series silt loam (Fine, smectitic, 

mesic Aquic Argiudolls) at Urbana, a Grantsburg silt loam (Fine-silty, mixed, active, mesic 

Oxyaquic Fragiudalfs) at Dixon Springs, and a Menfro silt loam (Fine-silty, mixed, superactive, 

mesic Typic Hapludalfs) at Perry. The experiment was arranged as a split-split-plot arrangement 

in a randomized complete block design with four replications with sorghum type as whole plots, 

sorghum hybrids as sub plots, and N fertilizer treatments as sub-sub plots. Two forage sorghum 

hybrids and two energy sorghum hybrids were planted each spring using a small plot drill (Great 

Plains No-Till Drill (606NT), Salina, KS). Planting dates ranged between 13 May and 9 June 

depending on the environment (Table 4.3). The energy hybrids, TAMUXH08001 and TX09007, 

both photoperiod-sensitive, were planted at 185,250 seeds ha-1spaced at 0.76 m. The forage 

hybrids, Graze All (a photoperiod-insensitive hybrid) and Graze. n. Bale (and photoperiodsensitive hybrid) were planted at 247,000 seeds ha-1

spaced at 0.19 m. All hybrids were obtained 
92

from Dr. William Rooney’s breeding program at Texas A&M University. Prior to planting, the 

seeds had been treated with a herbicide safener, Concep II (Syngenta, Macquarie, NSW). In 

2009, four N fertilizer treatments were applied at 0, 50, 100, and 150 kg N ha-1, while in 2010, 

five N fertilizer treatments were applied at 0, 56, 112, 168, and 224 kg N ha-1. The nitrogen 

fertilizer source, urea, was surface applied in each environment on the same day as planting 

(Table 4.3). The sub-sub plots were 5 m wide x 5 m long in 2009 and 5 m wide x 6.7 m long in 

2010. 

Preemergence herbicide, 2.2 kg a.i. ha-1of atrazine (6-chloro-N-ethyl-N'-isopropyl-1,3,5-

triazine-2,4-diamine) at Urbana and Perry, and 2.2 kg a.i. ha-1of atrazine and 1.4 kg a.i. ha-1sMetolachor (Acetamide, 2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl]-

,(S)) at Dixon Springs were applied to help control weeds. In addition, some manual weed 

removal was necessary at each site to minimize weed pressure and ensure proper establishment 

when weather conditions likely reduced the effectiveness of the pre-emergence herbicide. These 

conditions also resulted in slow, prolonged emergence. Weather data at Urbana were obtained at 

an on farm weather station for 2009 and 2010 growing season observations, while data for Dixon 

Springs, Perry, and 30-year weather averages were obtained from nearby weather stations from 

the Midwestern Regional Climate Center, a cooperative program of the Illinois State Water 

Survey and the National Climatic Data Center (Table 4.2). Accumulated growing-degree days 

were calculated with a base temperature of 11°C (Hammer et al., 1993).

Energy sorghum plant height was determined by measuring the height of the upper fully 

most expanded leaf on the main tiller of one-to-three randomly selected plants on four of five 

dates throughout the growing season (Table 4.3). On the same tillers, the number of fully 

expanded leaves was also determined by counting the number of nodes or visible leaves present 
93

on each tiller. These measurements were collected during GS I (vegetative growth from 

emergence to initiation of reproductive structures) and GS II (panicle initiation through booting 

and heading) growth stages (Gerik et al., 2003; Sorghum Management Guide, 2010). The focus 

of these measurements was on the photoperiod-sensitive energy sorghum hybrids 

(TAMUXH08001, and TX09007) which remained in the GS I stage throughout most of the 

growing season and only reached the GS II stage near the end of Sept. For the energy sorghum 

hybrids (TAMUXH08001, and TX09007), leaf area index (LAI) and the proportion of 

photosynthetic active radiation (PAR, 400-700 nm) intercepted by the crop canopy were 

measured on four dates during the 2009 and 2010 growing seasons at Urbana. Sampling dates in 

2009 were 13 July, 29 July, 29 Aug, and 18 Sept., while sampling dates in 2010 were 25 June, 14 

July, 12 Aug., and 31 Aug. The measurements were collected by measuring the PAR above the 

crop canopy using an external sensor (Model Quantum, LI-COR Biosciences) connected to a 

linear ceptometer (Model PAR-80, Decagon Devices, Inc.) which was used to measure the 

amount of PAR not intercepted by the crop canopy. These measurements were taken beneath the 

crop canopy between 10:00 and 14:00 hrs on sunny or mostly sunny days. Two to three 

subsamples were taken in each plot for each measurement date and each subsample was the 

average of ~20 independent readings. Light interception was determined by calculating the 

proportion of PAR intercepted by the crop canopy. Leaf area index was calculated for each 

subsample using the observations of radiation interception beneath and above the canopy, and 

zenith angle and leaf angle distribution (Decagon, 1994). For LAI, only readings with above 

canopy PAR that were at least 1400 nm were used in the statistical analysis. 

The forage sorghum hybrids had the capability of ratooning (regrowth after harvesting) 

and were harvested twice each season, once near the end of summer and a second time in the fall 
94

(Table 4.3). This was done per recommendation of Dr. William Rooney at Texas A&M 

University, plant breeder of hybrids used in this experiment, who suggested that forage sorghum 

be harvested two times each season and energy sorghum harvested one time in the fall or after 

the end of the growing season. In 2009 at Urbana, a second harvest for the forage sorghum 

hybrids was not obtained because of late planting (9 June), a late first harvest (3 Sept), and a 

relatively short and cool growing season (Table 4.3) that resulted in a lack of harvestable 

biomass for a second harvest in the fall. Harvesting was done by cutting a 1.216 m wide pass 

down the middle of each plot using a small plot biomass harvester with an electronic scale for 

determining harvestable wet biomass (Wintersteiger Cibus S harvester mounted with a Kemper 

forage chopper, Ames, IA), expect for Perry 2010 where harvesting was done by hand by cutting 

a 0.76 m pass down the middle of each energy hybrid plot and a 1.1216 m pass down the middle 

of each forage sorghum hybrid plot. Subsamples were collected from the wet biomass of each 

plot to determine percent moisture and dry biomass. Subsamples were dried in a forced air oven 

for at least 48 hr at 60°C and % moisture was used to calculate dry biomass. 

Statistical Analysis

In each environment, the experiment was arranged as a split-split-plot arrangement in a 

randomized complete block design with four replications with sorghum type as whole plots, 

sorghum hybrids as sub plots, and N fertilizer treatments as sub-sub plots. Data from Urbana 

2009 were analyzed separately from the data obtained in 2010. Environments and blocks were 

considered random while the effects of sorghum type, hybrid, and N rate were considered fixed. 

The non-linear function used to model the increase in energy sorghum plant height 

throughout the growing season was the logistic growth function,
95

 

 

 

 

 

 

[eq. 1]

Here, is energy sorghum plant height (meter units) measured throughout the growing 

season and x is the day of year (DOY). Three parameters describe the shape and spread of the 

function: 1) asymptote (asym) or maximum height achieved by the crop, 2) scale (scal) or the 

elapsed time between the crop achieving half and three quarters of it maximum height, and 3) 

inflection point (xmid) or DOY at which the crop achieves half of its maximum height. 

Parameters for each experimental unit (i.e. plot) were derived by fitting a logistic function to the 

data for each environment using the ‘nlme’ package (Pinheiro et al., 2009) of R statistical 

software (R Core, v. 2.12.1, 2010) by following principles in Pinheiro and Bates (2000). The 

logistic curves fitted to the data in each experimental unit were plotted visually to evaluate the 

agreement between the observed and fitted values and to identify possible outliers. Residuals 

were checked for patterns by plotting standardized residuals against their fitted values and plots 

were obtained using the ‘graphics’ and ‘lattice’ packages of R (R Core, v. 2.12.1, 2010). Once 

the parameter estimates were derived they were analyzed in the GLIMMIX procedure of SAS 

(SAS Institute, 2007) to evaluate the effects of environment, hybrid, and N rate on each logistic 

growth function parameter. 

Leaf area index, forage sorghum summer and fall harvest biomass yields, and total biomass 

yield analyzed in the GLIMMIX procedure of SAS (SAS Institute, 2007). Leaf area index 

measurements collected throughout the 2009 and 2010 growing seasons at Urbana were only 

used in if the above canopy PAR was at least 1400 nm. Before analysis, LAI values for each 

plot were averaged across subsamples before statistical analysis. Day of year was analyzed 

using a repeated measures technique with appropriate variance-covariance structure (Littell et al., 
96

2000, 2006). Least square means from 2009 and 2010 were then plotted against DOY for each N 

rate (Fig. 4.2). Forage sorghum summer and fall biomass yields were analyzed to evaluate the 

effect of environment, N rate, and hybrid on the biomass yield of each cutting. Total dry 

biomass yield achieved during the growing season (summer + fall harvest for forage sorghum, 

and fall harvest for energy sorghum) were analyzed to consider the effects of environment, 

sorghum type, hybrid, and N rate on the seasons total biomass productivity.

For each variable that was analyzed in the GLIMMIX procedure of SAS (SAS Institute, 

2007), it was assumed that the random effects of block and environment were independent 

normal random variables with expectations zero and their own respective variances, and that the 

errors had means of zero and common variances. Environments were considered random to 

account for different weather and other environmental conditions at each site which could not be 

controlled. Because of the split-split-plot arrangement sorghum hybrids were considered to be 

nested within sorghum type. Similarly, the blocks in each environment were unique to their 

associated environment and were thus considered to be nested in environment. Significance of 

random effects were calculated with a Wald Z test statistic using the COVTEST option in the 

GLIMMIX procedure of SAS (SAS Institute, 2007). Residuals were examined for normality and 

the assumption of common variances by inspection of residual plots. Preplanned comparisons 

and best linear unbiased predictions (i.e. means of random effects) and their interactions were 

calculated using estimate and contrast statements with appropriate degrees of freedom and 

standard errors in the GLIMMIX procedure of SAS (SAS Institute, 2007). Fisher’s least 

significant differences (LSD) were determined at the α = 0.5 level using appropriate degrees of 

freedom and standard errors. Pearson correlation coefficients and their respective P-values were

calculated using the CORR procedure of SAS (SAS Institute, 2007). Number of fully expanded 
97

leaves accumulated throughout the growing season before harvesting of energy sorghum hybrids 

exhibited a linear relationship when plotted against GDD. Simple linear regression lines were fit 

to these data for each environment (Fig. 4.3) using the ‘xyplot’ function of R (R Core, v. 2.12.1, 

2010). All other figures were created using the SGPLOT procedure of SAS (SAS Institute, 

2007) and SigmaPlot (v. 11.2, Systat Software, inc.). Pearson correlations coefficients were 

calculated to evaluate end of season plant height, end of season expanded leaf number, and dry 

biomass yields of energy sorghum. This was done by correlating the average of the final plant 

height and expanded leaf number measurements collected in each environment (Table 4.3) with 

energy sorghum biomass yield.

RESULTS AND DISCUSSION

Plant Height

For energy sorghum plant height in Urbana 2009, the main effect of N rate was 

significant for the asym (P = 0.0007), xmid (P = 0.0036) and scal (P = 0.0012) parameters but 

not for the hybrid x N rate interaction (P 0.3442) and main effect of hybrid (P 0.2259). 

This suggests that both energy sorghum hybrids (TAMUXH08001 and TX09007) accumulated 

plant height similarly throughout the growing season and were influenced in the same manner by 

N rate. As the season progressed, the differences in plant height due to N rate became more and 

more apparent, and by the end of the season there was a large difference between the maximum 

height (asym) of plots receiving 0 kg N ha-1and the plots receiving 50, 100, or 150 kg N ha

-1

(Fig. 4.1). When the average of the 50, 100, and 150 kg N ha-1rates were contrasted against the 

0 kg N ha-1rate, there was a significant difference for each logistic function parameter (P 

0.0018). More specifically, the asym or maximum height increased significantly from 0 kg N ha-
98

1

to 150 kg N ha-1(P = 0.0002) but the higher N rates (100 kg N ha-1and 150 kg N ha-1) were not 

significantly different from each other (P = 0.3124). 

For energy sorghum plant height in the 2010 environments, the effects of environment, N 

rate, and energy sorghum hybrid were evaluated for each logistic function parameters (eq. 1). It 

was found that the effect of environment was non-significant for the asym (P = 0.1753), xmid (P

= 0.2326) and scal (P = 0.1642) parameters, and that the hybrid x N rate interaction (P 0.5854)

and the main effect of hybrid (P 0.5785) were also non-significant for each logistic function 

parameter. Plant height responded less favorably in 2010 than in 2009 to N rate. The main 

effect of N rate in 2010 was only significant for the asym (P < .0001) but not for the xmid (P = 

0.7286) and scal (P = 0.8253) parameters. The greatest plant heights achieved of the three 2010 

environments occurred at the 168 kg N ha-1rate in each environment achieving 3.7 m in Dixon 

Springs 2010, 4.1 m in Perry 2010, and 3.8 m in Urbana 2010 (Fig. 4.1). This is contrasted with 

the greatest plant height in the Urbana 2009 environment which reached only 3 m at the 150 kg 

N ha-1rate (Fig. 4.1). The sorghum plants in 2010 likely grew taller than in 2009 because they 

were planted at least 2 weeks earlier (Table 4.3) and had a longer growing season. Additionally, 

the 2010 growing season was much warmer and had more precipitation during June through 

Sept. than the 2009 growing season (Table 4.2). Energy sorghum, can grow for 200 days in the 

right environment (typically southern environments like TX) and reach heights of 6 m (20 ft) 

(Sorghum Management Guide, 2010), however its ability to produce average heights up to 4.1 

meters in Perry, IL is quite remarkable, considering that Perry, IL is over 1300 km northeast of 

College Station, TX (driving distance), the area where these hybrids were developed. 
99

Leaf Area Index

Leaf area index expresses the ratio of surface leaf area to the ground area occupied by the 

crop. For LAI of the energy sorghum hybrids in the Urbana 2009 environment the N rate x DOY 

interaction (P = 0.0058), N rate (P = 0.0042), and DOY effects were significant (P < .0001). As 

the energy sorghum hybrids grew throughout the growing season, LAI increased significantly 

from the first sampling date on 13 July to the third sampling date on 29 Aug. (P < .0001) but 

then leveled off between the third sampling date and the fourth sampling date on 18 Sept. (P =

0.7748) (Fig. 4.2). Throughout the growing season, LAI was lower for the 0 kg N ha-1rate than 

for the other N rates (50 kg N ha-1, 100 kg N ha-1, and 150 kg N ha-1(P = 0.0014). In the Urbana 

2010 environment, the only significant main effect was DOY (P < .0001). Just as in 2009, LAI 

significantly increased in 2010 from the first sampling date on 25 June to the third sampling date 

on 12 Aug. (P < .0001) but then leveled off between the third sampling date and the fourth 

sampling date on 31 Aug. (P = 0.7985) (Fig. 4.2). The pattern of LAI responses to N rate was 

very similar to that of plant heights as the response of plant height was lower in 2010 than in 

2009. Leaf area index values were highly correlated with the proportion of PAR intercepted by 

the crop canopy in 2009 ( = 0.93) (P < .0001) and in 2010 ( = 0.95) (P < .0001).

Leaf Expansion and Development

The number of fully expanded leaves produced by the photoperiod-sensitive energy 

sorghum hybrids (TAMUXH08001, and TX09007) was linearly associated with GDD, with

greater a greater number of fully expanded leaves produced with greater heating units or GDD. 

The warmest environments, Dixon Springs 2010 and Perry 2010 accumulated 1906 and 1631 

GDD, respectively (Table 4.3). These were the two environments which accumulated the 

greatest number of fully expanded leaves, each accumulating an average of 28 leaves on the 
100

energy sorghum hybrids during the growing season. This is compared to typical grain sorghum 

hybrids (which are not photoperiod-sensitive) that accumulate between 15 and 19 leaves per 

plant depending on maturity group (Gerik et al., 2003). Even though the number of fully 

expanded leaves was similar for Dixon Springs 2010 and Perry 2010, plant height and biomass 

yield between the two environments were quite different (Fig. 4.1 and Table 4.4). 

Dry Biomass Yields

For total dry biomass yields in the Urbana 2009 environment, the effects of type (P =

0.0114) and N rate (P < .0001) were significant. The effect of hybrid was non-significant (P =

0.5445) and estimates of dry biomass yield were calculated only for sorghum type (Table 4.4). 

In Urbana 2009, dry biomass yields were significantly greater for energy sorghum than for 

forage sorghum (P = 0.0114). Preplanned comparisons showed that an N linear response was 

significant (P < 0.0001) and the linear slope was the same both sorghum types (P = 0.5924). 

Forage sorghum was first harvested on 3 Sept. and a lack of harvestable biomass limited a 

second cutting in the fall. The energy sorghum hybrids were allowed to grow the entire season 

and were not harvested until 12 Nov. It appears that energy sorghum hybrids had greater overall 

biomass potential than forage sorghum hybrids even if forage sorghum hybrids were harvested 

both in summer and fall as occured in 2010 (Tables 4.4 and 4.5). Increasing N application from 

0 kg N ha-1to 150 kg N ha-1improved energy sorghum biomass yields by 10.6 Mg ha-1and 

forage sorghum by 9.6 Mg ha-1. 

Biomass yields for forage sorghum in 2010 showed that summer harvest accounted for

approximately 67%, 66%, and 79% of the total biomass (summer + fall harvest) yield in the 

Urbana 2010, Dixon Springs 2010, and Perry 2010 environments, respectively. For the summer 

forage sorghum harvest, the N rate effect was significant (P = 0.0272) but not for the fall forage 
101

sorghum harvest (P = 0.2844). There was no difference among forage sorghum hybrids at the 

summer harvest (P = 0.7812), but there was a significant hybrid difference for the fall harvest (P

= 0.0240) with Graze All re-growing approximately 0.65 Mg DM ha-1 more biomass than Graze. 

n. Bale since the summer harvest. 

For total dry biomass yields in the 2010 environments, the effects of type (P = 0.0734)

and N rate (P = 0.0062) were significant, and again as in the Urbana 2009 environment, the 

hybrid main effect was non-significant (P = 0.2489). Estimate statements showed that energy 

sorghum yields were significantly greater than forage sorghum yields in the Perry 2010 (P =

0.0018) and Urbana 2010 (P = 0.0311) environments but not in the Dixon Springs 2010 

environment (P = 0.2104). Forage sorghum yields in each 2010 environment were quite similar 

and their response to increasing levels of N tended to follow a similar pattern (Table 4.4) and 

when contrasting forage sorghum dry biomass yields among environments the only significant 

difference was between Perry 2010 and Urbana 2010 (P = 0.0121). Overall in 2010, there was a 

significant increase in dry biomass yield for forage sorghum between the 0 kg N ha-1rate and the 

168 kg N ha-1rate, but dry biomass yields did not increase from the 168 kg N ha-1rate to the 224 

kg N ha-1rate (Table 4.4). Energy sorghum dry biomass yields were more influenced by 

environment than forage sorghum, and were less stable from environment to environment (Table 

4.4). Energy sorghum yields were the highest in the Perry 2010 environment when compared 

with Urbana 2010 (P = 0.0811) and Dixon Springs 2010 (P = 0.0072) which was the lowest. 

The soil at Dixon Springs (southern IL) is generally of lower quality (i.e. lower organic matter, 

total N) than Urbana (east central IL) and Perry (west central IL) (Wander and Bollero, 1999). 

Also this environment had relatively low levels of P and K at planting compared to the other 

environments, and the precipitation in mid-to-late summer was very low totaling 74 mm 
102

precipitation in July and Aug. In addition to being dry, the temperature was also very high 

during this time period with 15 days > 35°C (Table 4.2). It appears that this combination of 

lower soil quality and hot and dry conditions created an environment in which energy sorghum 

hybrids were unable to grow as well as in Perry 2010 and Urbana 2010. In addition to lower dry 

biomass yields, plant height was shorter for energy sorghum in the Dixon Springs 2010 

environment (Fig. 4.1).

Correlations of end of season plant height, end of season fully expanded leaf number and 

energy sorghum biomass yield (Mg DM ha-1) in 2009 showed positive linear relationships. In 

the Urbana 2009 environment end of season plant height and biomass yield was highly correlated 

( = 0.87) (P < .0001), while biomass yield and end of season fully expanded leaf number ( = 

0.31) (P = 0.0815), and end of season plant height and end of season fully expanded leaf number 

( = 0.35) (P = 0.0477) showed less positive relationships. The strong positive relationship end 

of season plant height and biomass yield in 2009 is likely related to the strong N rate response 

observed for both plant height and biomass yield (Fig. 4.1 and Table 4.4). In the 2010 

environments, the relationship between these two variables was much less pronounced than in 

2009, with the following correlations between end of season plant height and biomass yield: 

Urbana 2010 ( = 0.19) (P = 0.2903), Dixon Springs 2010 ( = 0.39) (P = 0.0137), and Perry 

2010 ( = 0.19) (P = 0.2422). However, end of season plant height and end of season fully 

expanded leaf number in 2010 was generally more positive than in 2009: Urbana 2010 ( = 0.65) 

(P < .0001), Dixon Springs 2010 ( = 0.36) (P = 0.0243), and Perry 2010 ( = 0.51) (P < .0007).

Plant height accumulation and biomass yield response to N in each of the 2010 

environments were less pronounced than in the Urbana 2009 environment likely because the 

residual soil NO3

-

present in the soil in the Urbana 2009 environment at planting was very low 
103

(5.5 mg kg-1in the upper 23 cm of soil). This was due to a wetter than normal spring (Table 4.2) 

which limited early planting and probably caused some N loss via leaching. Thus, the plants in 

the Urbana 2009 environment responded very favorably to N fertilizer application, thus creating 

a larger disparity between shorter plants with lower yields and taller plants with greater yields. 

In 2010, each environment had higher residual NO3

-

levels than Urbana 2009, which likely 

limited the N response from being greater than it was. Due to the strong linear response of dry 

biomass yield to N rate in the Urbana 2009 environment and the variability in dry biomass yields 

among the 2010 environments it is difficult to suggest an optimal N rate recommendation when 

growing forage or energy sorghum for biomass in IL. These results suggest the need to continue 

evaluating sorghum grown for biomass in these IL sites as well as other sites to increase the 

number of site-years to predict dry biomass yield potential and recommended N rates. 

CONCLUSIONS

The results of this study show that forage and energy sorghum possess great potential as 

biomass crops for IL. Environment had a major impact on plant growth and total biomass yields, 

and the degree to which sorghum responded to N fertilizer rate. In some cases these differences 

were explained by differences in leaf area index and plant height accumulation. Even though 

forage sorghum was harvested twice annually (summer and fall), energy sorghum harvested once 

in the fall had greater biomass yields in each environment. Biomass yield, LAI, and plant height 

response to N rate were observed up to 150 kg N ha-1in 2009 and up to at least 168 kg N ha-1in 

2010. Photoperiod-sensitive forage and energy sorghum allow vegetative growth to continue 

late into until late Sept. in IL and therefore continue accumulating biomass until late Sept. 

Because forage and energy sorghum are annual crops they provide a unique crop option to IL 

producers and stakeholders interested in biomass crop production. 
104

Acknowledgments

The authors acknowledge the Energy Biosciences Institute and the Energy Biosciences 

Institute Energy Farm at the University of Illinois Urbana-Champaign for providing funding, 

land, and labor support for this study.

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107

TABLES

Table 4.1. Soil properties from environments in 2009 and 2010 (Urbana 2009, Urbana 2010, Dixon Springs 2010, 

and Perry 2010). Samples were obtained either within the plots before fertilization occurred or adjacent to the plots 

at planting or early in the growing season and are expressed as mean values ± standard deviation.

Environment depth pH SOM† P K NH4-N NO3-N

cm % ----------------------------mg kg-1---------------------------

Urbana 2009 0-23 6.5 (0.3)‡2.8 (0.6) 38 (12) 207 (41) - 5.5 (1.7)

Urbana 2010 0-23 6.4 (0.6) 3.3 (0.5) 51 (29) 200 (412) 5.6 (1) 23.8 (6.2)

Dixon Springs 2010 0-15 7.1 (0.1) 2.4 (0.2) 41 (15) 110 (8) 10.6 (2) 30.5 (7.7)

15-30 6 (0.5) 1.4 (0.2) 13 (3) 81 (4) 11.4 (2.2) 22.6 (5.8)

Perry 2010 0-10 6.1 (0.2) 2.2 (0.1) 24 (2) 175 (13) 11.2 (1.8) 23.3 (8.7)

10-20 6.5 (0) 1.7 (0.2) 14 (3) 130 (20) 9.9 (1.6) 7.9 (1.4)

20-30 6.2 (0.2) 1.4 (0.2) 16 (3) 160 (4) 8.9 (1.1) 4.9 (0.8)

†

soil organic matter (SOM)

‡

standard deviation in parenthesis, and n=4, 5, 3, and 4 respectively for Urbana-2009, Urbana-2010, Dixon 

Springs-2010, and Perry-2010.
108

Table 4.2. Monthly average minimum (min.) and maximum (max.) air temperature, 30-yr temperature averages, number of 

days with temperature > 35°C, and precipitation in each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and 

Perry 2010) during the growing season (May-October).

Environment weather May June July Aug. Sept. Oct. average/total

Urbana 2009 average min. (°C) 11.1 16.7 15.7 15.5 12.9 5.3 12.9

30-yr average min. (°C) 10.6 15.8 18.0 16.9 12.4 6.1 13.3

average max. (°C) 22.5 27.7 25.2 26.3 24.0 14.8 23.4

30-yr average max. (°C) 23.1 28.1 29.6 28.4 25.3 18.4 25.5

no. days temp. > 35°C 0 0 0 0 0 0 0

precipitation, mm 145 92 137 94 22 201 692

30-year average precipitation, mm 122 107 119 111 82 71 612

Urbana 2010 average min. (°C) 12 18.1 18.6 17.9 12.3 5.6 14.1

30-yr average min. (°C) 10.6 15.8 18 16.9 12.4 6.1 13.3

average max. (°C) 23.3 28.4 29.6 30.2 26.1 21 26.4

30-yr average max. (°C) 23.1 28.1 29.6 28.4 25.3 18.4 25.5

no. days temp. > 35°C 0 0 0 0 0 0 0

precipitation, mm 75 199 82 55 75 34 519

30-year average precipitation, mm 122 107 119 111 82 71 612

Dixon Springs 2010 average min. (°C) 16.1 19.4 20.4 18 14.2 6.7 15.8

30-yr average min. (°C) 12.8 17.3 19.6 18.4 14.5 8.2 15.1

average max. (°C) 26.7 32.8 34 34.4 29.8 25 30.4

30-yr average max. (°C) 26.1 30.4 32.4 32 28.6 22.8 28.7

no. days temp. > 35°C 0 0 3 12 0 0 15

precipitation, mm 18 104 51 23 86 25 306

30-year average precipitation, mm 131 104 95 91 83 80 585

Perry 2010 average min. (°C) 11.4 17.8 19.2 18.1 13.1 4.9 14.1

30-yr average min. (°C) 11.4 16.7 18.7 17.2 12.4 6.2 13.8

average max. (°C) 24.2 31.4 32.4 31.8 26.8 21.9 28.1

30-yr average max. (°C) 23.1 27.8 30.2 29.3 25.4 19.2 25.8

no. days temp. > 35°C 0 0 1 6 0 0 7

precipitation, mm 143 292 301 26 62 52 876

30-year average precipitation, mm 104 84 103 77 82 72 523
109

Table 4.3. Planting date, dates of plant height and fully expanded leaf number measurements, accumulated growing 

degree days, and harvest dates for each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and Perry 

2010). 

plant height and fully expanded leaf 

number measurements

-----harvest dates-----

Environment planting date GDD†

summer‡fall§

Urbana 2009 9 June 13 July, 29 July, 19 Aug., 18 Sept., 7 Oct. 1129 3 Sept. 12 Nov

Urbana 2010 24 May 25 June, 21 July, 31 Aug., 27 Sept. 1517 12 Aug. 9 Nov.

Dixon Springs 2010 13 May 30 June, 19 July, 5 Aug., 8 Dec.

¶

1906 5 Aug. 8 Dec.

Perry 2010 26 May 1 July, 23 July, 17 Aug., 23 Nov.¶1631 17 Aug. 23 Nov.

†

growing degree days (GDD) with a base temperature of 11°C accumulated since planting until fall harvest or first 

fall frost, whichever occurred first.

‡

forage sorghum was cut two times, once in summer and once in the fall.

§

energy sorghum was only cut one time in the fall.

¶ measurements were taken during the fall harvest, but it was assumed that these final plant heights and number of 

fully expanded leaves were achieved the day before the first killing frost. The first killing frost for these two 

environments occurred on 5 Oct. and 4 Oct. for Dixon Springs 2010 and Perry 2010, respectively.
110

Table 4.4. Total sorghum biomass yield (Mg DM ha-1) obtained from each environment in 

2009 (Urbana 2009) and 2010 (Urbana 2010, Dixon Springs 2010, and Perry 2010) for each 

N rate (kg N ha-1) and their respective ANOVA. Biomass yields were obtained using least 

square means and in the case of the 2010 environments were estimated as best linear unbiased 

predictors (aka means of random variables). 

environment sorghum -----------------kg N ha-1

---------------- LSD†

type 0 50 100 150

Urbana 2009 energy 13.1 15.4 21.6 23.7 3.63

forage 7.0 9.7 14.1 16.7 3.63

----------------------kg N ha-1----------------------

0 56 112 168 224

Urbana 2010 energy 22.6 24.8 25.9 27.2 30.1

forage 16.9 18.9 19.4 20.4 20.5

Dixon Springs 2010 energy 17.8 20.0 21.2 22.4 25.3

forage 15.7 17.7 18.2 19.2 19.3

Perry 2010 energy 27.6 29.8 31.0 32.2 35.1

forage 14.4 16.4 16.9 17.9 18.0

all 2010 environments energy 22.7 24.9 26.0 27.2 30.1 2.96

forage 15.7 17.7 18.2 19.2 19.2 2.84

ANOVA and P-values

--------------Urbana 2009 ANOVA------------- ----------2010 environments ANOVA----------

 Type 0.0114 environment‡> 0.99

 hybrid 0.5445 Type 0.0734

 N rate <.0001 hybrid 0.2489

 type x N rate 0.9056 N rate 0.0062

 N rate x hybrid 0.6613 type x N rate 0.2311

 N rate x hybrid 0.3240

† LSD values for comparing dry biomass yield values across N rate within sorghum type.

Differences greater than LSD values are significantly different at the α = 0.05 level.

‡ environment is a random effect and when its covariance parameter estimate was equal to zero it’s pvalue (based upon a Wald Z statistic) was set to > 0.99.
111

Table 4.5. Forage sorghum biomass yields for summer harvest and 

fall harvests for each 2010 environment (Urbana 2010, Dixon 

Springs 2010, and Perry 2010) and ANOVA for each harvest. 

Biomass yields were obtained using least square means and in the 

case of individual environments were estimated as best linear 

unbiased predictors (aka means of random variables).

Environment/source N rate

kg ha-1

summer

Mg DM ha-1

fall

Mg DM ha-1

Urbana 2010 0 10.5 6.3

56 12.6 6.4

112 13.1 6.0

168 14.1 6.5

224 14.0 6.7

Dixon Springs 2010 0 10.2 5.9

56 12.0 6.0

112 11.9 5.7

168 13.0 6.2

224 13.0 6.4

Perry 2010 0 10.9 3.2

56 12.7 3.4

112 13.0 3.0

168 14.2 3.5

224 14.0 3.7

--------------------------------------------

all 2010 environments 0 10.6 5.1

56 12.4 5.3

112 12.7 4.9

168 13.8 5.4

224 13.6 5.6

LSD†1.91 0.64

ANOVA ------------P-values------------

environment 0.2986 0.1632

hybrid 0.7812 0.0240

N rate 0.0272 0.2844

hybrid x N rate 0.1743 0.7145

† LSD values for comparing dry biomass yield of the 2010 

environments grouped together by N rate.
112

FIGURES

Figure 4.1. Energy sorghum plant height (m) plotted against day of year (DOY) and fit with logistic growth functions averaged across 

hybrids for each N rate at each location in each year.

Urbana 2009

day of year

180 200 220 240 260 280

height, m

0

1

2

3

4

0 kg N ha-1

50 kg N ha-1

100 kg N ha-1

150 kg N ha-1

Urbana 2010

day of year

180 200 220 240 260 280

height, m

0

1

2

3

4

0 kg N ha-1

56 kg N ha-1

112 kg N ha-1

168 kg N ha-1

224 kg N ha-1

Dixon Springs 2010

day of year

180 200 220 240 260 280

height, m

0

1

2

3

4

0 kg N ha-1

56 kg N ha-1

112 kg N ha-1

168 kg N ha-1

224 kg N ha-1

Perry 2010

day of year

180 200 220 240 260 280

height, m

0

1

2

3

4

0 kg N ha-1

56 kg N ha-1

112 kg N ha-1

168 kg N ha-1

224 kg N ha-1
113

Figure 4.2. Energy sorghum leaf area index (LAI) and interception of photosynthetic active radiation (PAR) plotted against day of year 

(DOY) and averaged across hybrids for each N rate at Urbana in 2009 and 2010.

Urbana 2009

day of year

190 200 210 220 230 240 250 260 270

LAI

0

1

2

3

4

5

0 kg N ha-1

50 kg N ha-1

100 kg N ha-1

150 kg N ha-1

Urbana 2010

day of year

170 180 190 200 210 220 230 240 250

LAI

0

1

2

3

4

5

0 kg N ha-1

56 kg N ha-1

112 kg N ha-1

168 kg N ha-1

224 kg N ha-1

Urbana 2009

day of year

190 200 210 220 230 240 250 260 270

intercepted PAR 20

40

60

80

100

0 kg N ha-1

50 kg N ha-1 

100 kg N ha-1

150 kg N ha-1

Urbana 2010

day of year

170 180 190 200 210 220 230 240 250

intercepted PAR

0

20

40

60

80

100

0 kg N ha-1

60 kg N ha-1

112 kg N ha-1

168 kg N ha-1

224 kg N ha-1
114

Figure 4.3. Number of fully expanded leaves of photoperiod-sensitive energy 

sorghum hybrids plotted against growing degree days (GDD) with a base 

temperature of 11°C accumulated since planting, and simple linear regression lines 

for each environment (Urbana 2009, Urbana 2010, Dixon Springs 2010, and Perry 

2010).
115

CHAPTER 5

Supplementary Information to Ch. 4

This chapter summarizes some additional analysis from the forage and energy sorghum 

field study in Ch. 4. 

In an effort to develop an understanding of the N response that sorghum exhibits, 

quadratic plus plateau curves were fit to the total biomass yield data when possible. A quadratic 

plus plateau as opposed to a quadratic model, was implemented because of research in corn (Zea 

mays L.) that has shown recommendations based on and quadratic model result in biases and 

over predictions (Bullock and Bullock, 1994a). The quadratic function tends to predict a decline 

in crop yield with increasing levels of N beyond the optimal N rate (Bullock and Bullock, 

1994a), calculated by setting the first derivative equal to zero. This prediction is generally not 

true unless extremely excessive N rates are applied that may ultimately cause lodging or other 

crop problems due to excessive N application. Additionally, the quadratic response model may 

overestimate yields for the predicted optimum N rate (Cerrato and Blackmer, 1990).

The quadratic plus plateau model has been shown to best describe yield responses to N in 

corn when compared to several other N response functions (linear-plus-plateau, quadratic, 

exponential, and square root) (Cerrato and Blackmer, 1990). Extending this model to sorghum 

grown for biomass is reasonable due to the similarities between the two crops. In the context of 

dry biomass production the quadratic plus-plateau model can be described as:

 

 

 

[eq. 2]

 
116

where is the biomass yield (Mg DM ha-1) and is the N application rate (kg N ha-1), is the 

intercept, is the linear coefficient, is the quadratic coefficient, and is the rate of N 

fertilization which occurs at the intersection of the quadratic response and the plateau ( ) 

portions of the model. The critical rate of fertilizer can be defined as the N rate required to 

achieve maximum dry biomass yield (Max N).

Where model convergence was possible, a quadratic plus plateau curve was fit to each 

sorghum type in each 2010 environment using the NLMIXED procedure of SAS (SAS Institute, 

2007). Plateau dry biomass yields and Max N rates were derived from each response function 

and are shown in Table 5.1. Max N rates are assumed to be the N rates that maximize dry 

biomass yields, and not profits, since they do not consider input costs, risk, or other stochastic 

events that can optimize or minimize a farmer’s production objective (Bullock and Bullock, 

1994b). Where convergence of a quadratic plus plateau model could not be achieved, or it was 

observed that the relationship between dry biomass yield and N rate was linear, a linear model 

was fit to the data. This was the case for both sorghum types in the Urbana 2009 environment 

and for energy sorghum in the Perry 2010 environment (Fig. 5.1). Residuals (observed yields 

minus predicted yields) were plotted against N rate for each environment (Fig. 5.1). Based upon 

the price of Mar. urea (46% N) from 2006 to 2011 (USDA/ERS, 2011), three prices of urea 

($362 ton-1($328.3 Mg-1), $448 ton-1($406.3 Mg-1), and $552 ton-1($500.6 Mg-1) and respective 

unit prices of a kg of N ($0.87 kg-1 N, $1.07 kg-1 N, and 1.32 kg-1 N), economical optimum N 

rates (EONR) were calculated by considering three theoretical prices of a Mg of dry biomass: 

$35 Mg-1, $50 Mg-1, and $65 Mg-1(Table 5.1). These economical optimum N rates were 

calculated ex post (after the fact) (Bullock and Bullock, 1994b) using the following equation:
117

 

 

 

 

 

 

[eq. 3]

where is the cost of N fertilizer ($ kg-1), is the price of dry biomass ($ Mg-1), is the linear 

coefficient from eq. 2, and is the quadratic coefficient from eq. 2. Generally, it is 

recommended that an ex ante (expected before the event) economical optimum N rate be 

calculated based upon several years of ex post rates calculated for a given site or location 

(Bullock and Bullock, 1994b), but this was not possible due to the limited amount of data 

available for each site. The variation within and among environments, shown by the varying 

levels of plateau yields, Max N, EONR, and the difference in functional form (linear vs. 

quadratic plus plateau) fit to the data suggest the need to continue researching sorghum biomass 

production in IL so that sufficient data is available to make reliable predictions of yield and 

economical optimum N rates that should be applied to meet farmers objectives. In addition, 

plant breeding efforts may continue to improve dry biomass yield potential, but also increase the 

ability of the crop to respond favorably and more consistently to agronomic inputs such as N 

fertilizer. It is apparent, however that the price of a Mg of dry biomass and a the price of N 

fertilizer can alter the rate of N that should be applied to meet an economic goal. As the price of 

N increased, the EONR decreased, however as the price of a Mg of dry biomass increased, the 

EONR also increased (Table 5.1). 

Measurements of leaf greenness for all hybrids were taken using a SPAD meter (Minolta 

Camera Co., Ltd., Japan) at 3 different time periods (14-15 July, 30 July, and 20 Aug.) 

heretofore referred to as early-summer, mid-summer, and late-summer, respectively. In 2010, 

SPAD readings were taken at two different time periods in the Urbana 2010 (25 June, and 21-22 

July), Dixon Springs 2010 (30 June, and 19 July), and Perry 2010 (11 July, and 23 July), 

heretofore referred to as early-summer and mid-summer, respectively. The sampling time 
118

periods in 2009 and 2010 each occurred during the GSI and GSII growth stages for photoperiodsensitive hybrids and the into the GSIII stage for the single photoperiod-insensitive hybrid 

(Graze All) (Gerik et al., 2003; Sorghum Management Guide, 2010). On each sampling date, 

eight SPAD reading were taken from each plot on eight separate tillers from eight representative 

plants and then averaged to produce one SPAD reading per plot. Readings were taken from the 

upper fully most expanded leaf of each selected tiller. From approximately the same location the 

SPAD readings were collected, three-one cm diameter leaf disc samples were taken across the 

leaf (one on the midrib and two on either side of the midrib) using a leaf puncher (Precision 

Machine Co., Lincoln, NE). This resulted in 24 leaf disks that were aggregated for each plot and 

were weighed wet and dried in a forced air oven at 48°C for at least 48 hours. Dried samples 

were ground and TN and TC were determined us a CHN analyzer (Costech Analytical Elemental 

Combustion System, Costech 4010 model, Valencia, CA). Leaf TC and leaf TN were 

determined by combustion in the presence of oxygen and appropriate standard curves and 

verification of proper analysis were determined using acetanilide and apple leaf samples 

(National Institute of Standards and Technology, Gaithersburg, MD). Subsamples that had been 

collected at harvest were dried in a forced air oven were then ground and used to determine 

harvest TN and TC following the same protocol as described above for the leaf punch samples. 

For forage sorghum, only samples coming from the first harvest were used in the statistical 

analysis.

SPAD, leaf TN, leaf TC, harvest TN, and harvest TC were each analyzed in the GLIMMIX 

procedure of SAS (SAS Institute, 2007). For SPAD, leaf N, and leaf C, statistical analysis, time 

of sampling (sampling period) was considered a fixed variable and was modeled as a split in 

time, because in 2010 there were only two sampling periods in each environment, reducing the 
119

need to consider this variable as a repeated measurement. In the Urbana 2009 environment, the 

first sampling period (14-15 July) for leaf TN and leaf TC was not used in the analysis because 

of errors resulting from the CHN analysis. Each of these variables were analyzed in the 

GLIMMIX procedure of SAS (SAS Institute, 2007), and statistical analysis followed the same 

protocol as in Ch. 4.

SPAD meter readings allow the relative comparison of plants showing varying levels of 

leaf greenness and chlorophyll concentration (Bullock and Anderson, 1998; Markwell et al., 

1995). SPAD readings can indicate plant N stress (Ruiz Diaz et al., 2008) and have been related 

to yield response to N in corn (Zea mays. L) (Scharf et al., 2006) and leaf TN in switchgrass 

(Panicum virgatum L.) (Madakadze et al., 1999). In the Urbana 2009 environment there was a 

moderate, yet positive significant correlation between SPAD and N rate ( = 0.69) (P < .0001) 

throughout the growing season. The correlation between SPAD and leaf TN during the last two 

sampling periods (mid-summer and late-summer) was also significant (P < .0001) but the 

correlation was much lower ( = 0.41). Correlations between SPAD meter readings and leaf TN 

have also been shown in corn, although correlations were low early in the season and increased 

as the crop moved into the reproductive stage (Bullock and Anderson, 1998). For SPAD, there 

was a significant N rate x time period x type interaction (P = 0.035) and a significant type x time 

period interaction (P < .0001), although readings generally declined from the early-summer to 

late summer for both sorghum types and almost all hybrids (Table 5.2). Each hybrid responded 

similarly to N, but was slightly different for each time period (Table 5.2). SPAD meter readings 

and leaf TN increased significantly from 0 kg N ha-1to 100 kg N ha-1but then leveled off 

between 100 kg N ha-1and 150 kg N ha-1(Table 5.2). SPAD meter readings also differed among 

sorghum hybrids (Table 5.2) which has also been shown in corn (Bullock and Anderson, 1998). 
120

There was a significant N rate x time period interaction for leaf TN (P < .0001) which increased 

from mid-summer to late-summer for each hybrid (Table 5.2). The only significant effect for 

leaf TC was time period (P = 0.0051) and leaf TC increased significantly for the energy sorghum 

hybrids from mid-summer to late- summer, but not for the forage sorghum hybrids (Table 5.2).

For the 2010 environments, there were moderate, yet positive significant correlations 

between SPAD and TN ( = 0.49) (P < .0001), and SPAD and N rate ( = 0.60) (P < .0001) at 

the early-summer measurements. The correlation between SPAD and TN ( = 0.30), and SPAD 

and N rate ( = 0.51) decreased in mid-summer although the correlation were still significant (P

< .0001). The effect of environment was non-significant for SPAD (P > .99), leaf TN (P = 

0.2817), and leaf TC (P = 0.4285). For SPAD, there was a significant time period x hybrid 

interaction (P = 0.0925) and SPAD measurements increased from early-summer to mid-summer 

for each hybrid although there were differences between hybrids (Table 5.3). SPAD meter 

readings and leaf TN increased significantly from 0 kg N ha-1to 112 kg N ha-1but then leveled 

off between 112 kg N ha-1and 168 kg N ha-1(Table 5.3). Leaf TN also differed at each sampling 

period and decreased from early-summer to late-summer for each sorghum type and each 

sorghum hybrid. All leaf TC effects were non-significant (Table 5.3). 

Harvest TN levels were at least half of the leaf TN levels taken during the growing 

season (Tables 5.2, 5.3, and 5.4). Harvest TN samples were a composite sample of the entire 

plant including stems, leaves, and in some cases panicles, while leaf TN samples only contained 

leaf material. Harvest TN was significant only for N rate (P = 0.0876) in the Urbana 2009 

environment, while only type was significant for the 2010 environments (P = 0.0589). Harvest 

TN increased significantly from 0 kg N ha-1to 150 kg N ha-1in the Urbana 2009 environment 

and from 0 kg N ha-1to 150 kg N ha-1in the 2010 environments. The effect of environment was 
121

non-significant for the 2010 environments, but energy sorghum had significantly lower harvest 

TN levels than forage sorghum. This is likely because the forage sorghum harvest samples came 

from the summer harvest when the plants were still green, while the energy sorghum harvest 

samples came from the fall harvest, which, for Dixon Springs and Perry, had begun field drying 

at harvest time. 

REFERENCES

Bullock, D.G., and D.S. Anderson. 1998. Evaluation of the Minolta SPAD-502 cholorphyll 

meter for nitrogen management in corn. J. Plant Nutr. 21:741–755.

Bullock, D.G., and D.S. Bullock. 1994a. Quadratic and Quadratic-Plus-Plateau Models for 

Predicting Optimal Nitrogen Rate of Corn: A Comparison. Agron. J. 86: 191-195.

Bullock, D.S., and D.G. Bullock. 1994b. Calculation of optimal nitrogen fertilizer rates. Agron. 

J. 86: 921-923.

Gerik, T. B. Bean, and R. Vanderlip. 2003. Sorghum Growth and Development. Texas AgriLife 

Extension publication, Texas A&M University. 

Madakadze, I.C., K.A. Stewart, R.M. Madakadze, P.R. Peterson, B.E. Coulman, and D.L. Smith. 

1999. Field Evaluation of the Chlorophyll Meter to Predict Yield and Nitrogen Concentration 

of Switchgrass. J. Plant Nutr. 22:1001-1010. 

Markwell, J., J.C. Osterman, and J.L. Mitchell. 1995. Calibration of the Minolta SPAD-502 leaf 

chlorophyll meter. Photosynth. Res. 46:467–472.

Ruiz Diaz, D.A., J.A. Hawkins, J.E. Sawyer, and J.P. Lundvall. 2008. Evaluation of In-Season 

Nitrogen Management Strategies for Corn Production. Agron. J. 100:1711–1719.

Scharf, P.C., S.M. Brouder, and R.G. Hoeft. 2006. Cholorphyll Meter Readings Can Predict 

Nitrogen Need and Yield Response of Corn in the North-Central USA. Agron. J. 98:655–

665.

Sorghum Management Guide. 2010. Managing High-Biomass Sorghum as a Dedicated Energy 

Crop. Blade Energy Crops. Available online at 

http://www.bladeenergy.com/Bladepdf/Blade_SorghumMgmtGuide2010.pdf (accessed 2

May 2011; verified 22 June 2011).

USDA/ERS. 2011. Average U.S. farm prices of selected fertilizers. Available at 

http://www.ers.usda.gov/Data/FertilizerUse/ (accessed 15 May 2011; verified 22 June 2011). 
122

TABLES

Table 5.1. Plateau biomass yields based upon quadratic plus plateau response 

curves and associated N rate (max N) required to achieve plateau biomass yields at 

each 2010 environment for each sorghum type. Ex post economic optimum N rates 

using based upon three prices of a unit of N ($0.87 kg-1 N, $1.07 kg-1 N, and 1.32 kg1 N) and three prices for a Mg of dry biomass ($35 Mg-1

, $50 Mg-1, and $65 Mg-1).

plateau biomass yields (Mg DM ha-1)

environment sorghum 

type

plateau yield 

Mg DM ha-1

max N

kg N ha-1

Urbana 2010 energy 28.4 187.4

forage 21.1 193.7

Dixon Springs 2010 energy 21.2 41.8

forage 18.3 47.8

Perry 2010 energy†- -

forage 17.9 214.9

economic optimum N rate (kg N ha-1)

-----------$ Mg-1dry biomass---------

environment sorghum 

type $ kg-1 N $35 $50 $65

Urbana 2010 energy $0.87 115.42 137.02 148.65

$1.07 98.87 125.43 139.74

$1.32 78.18 110.95 128.60

forage $0.87 96.63 125.75 141.43

$1.07 74.32 110.13 129.41

$1.32 46.43 90.61 114.39

Dixon Springs 2010 energy $0.87 35.66 37.48 38.47

$1.07 34.25 36.50 37.72

$1.32 32.50 35.28 36.77

forage $0.87 35.66 37.48 38.47

$1.07 34.25 36.50 37.72

$1.32 32.50 35.28 36.77

Perry 2010 energy $0.87 - - -

$1.07 - - -

$1.32 - - -

forage $0.87 62.16 107.97 132.65

$1.07 27.05 83.40 113.74

$1.32 na‡52.68 90.11

† a quadratic plus plateau response curve was not fit for the energy sorghum type at 

Perry 2010.

‡ economical optimum N rate calculated was less than 0. 
123

Table 5.2. SPAD, leaf TN, and leaf TC measurements from Urbana 2009 showing 

results by N rate, for the type x time period, and time period x hybrid(type) interaction. 

Analysis of variance (ANOVA) for each variable is shown at bottom of table.

SPAD leaf TN leaf TC

N rate, kg N ha-1no units g kg-1g kg-1

 0 34.5 1.87 38.5

 50 38.1 1.91 40.0

 100 43.0 2.37 41.4

 150 44.9 2.23 42.6

 LSD†1.96 0.354 4.25

type x time period

 Energy, early-summer 42.5 - -

 Energy, mid-summer 41.2 1.96 38.6

 Energy, late-summer 39.2 2.17 44.0

 Forage, early-summer 41.6 - -

 Forage, mid-summer 37.5 2.06 38.6

 Forage, late-summer 38.6 2.19 41.3

 LSD‡3.37 0.561 4.32

time period x hybrid

 TAMUXH08001, early-summer 42.3 - -

 TAMUXH08001, mid-summer 41.9 2.04 40.2

 TAMUXH08001, late-summer 39.7 2.29 42.8

 TX09007, early-summer 42.8 - -

 TX09007, mid-summer 40.6 1.88 37.0

 TX09007, late-summer 38.7 2.05 45.3

 Graze All, early-summer 42.5 - -

 Graze All, mid-summer 39.0 2.30 38.1

 Graze All, late-summer 42.2 2.59 40.3

 Graze. n. Bale, early-summer 40.8 - -

 Graze. n. Bale, mid-summer 36.0 1.82 39.0

 Graze. n. Bale, late-summer 35.0 1.79 42.2

 LSD§2.97 0.599 5.87

ANOVA -----------------------P-values------------------------

 Type 0.175 0.7542 0.3855

 hybrid 0.0008 0.0016 0.7816

 N rate <.0001 0.0136 0.245

 type x N rate 0.3166 0.327 0.7843

 N rate x hybrid 0.3994 0.3042 0.4382

 time period <.0001 0.1469 0.0051

 type x time period <.0001 0.7276 0.3297

 time period x hybrid <.0001 0.6014 0.355

 N rate x time period <.0001 0.01 0.5687

 type x Nrate x time period 0.035 0.6463 0.3943

 N rate x time period x hybrid 0.3697 0.1118 0.8613

† LSD values for comparing within N rate.

‡ LSD values for comparing values within type x time period interaction.

§ LSD values for comparing values within time period x hybrid(type) interaction.

Differences greater than LSD values are significantly different at the α = 0.05 level
124

Table 5.3. SPAD, leaf TN, and leaf TC measurements from all 2010 environments 

(Urbana 2010, Dixon Springs 2010, and Perry 2010) showing results by N rate, for the 

type x time period, and time period x hybrid(type) interaction. Analysis of variance 

(ANOVA) for each variable is shown at bottom of table.

SPAD leaf TN leaf TC

N rate, kg N ha-1no units g kg-1g kg-1

 0 38.6 1.94 34.5

 56 42.2 2.12 35.0

 112 45.0 2.44 35.3

 168 46.0 2.61 34.6

 224 46.3 2.60 35.2

 LSD†1.78 0.205 2.39

type x time period

 Energy, early-summer 43.9 2.83 37.1

 Energy, mid-summer 46.1 1.97 34.0

 Forage, early-summer 41.0 2.41 35.5

 Forage, mid-summer 43.4 2.14 33.1

 LSD‡4.18 0.714 19.95

time period x hybrid

 TAMUXH08001, early-summer 43.4 2.81 37.7

 TAMUXH08001, mid-summer 46.1 2.00 34.7

 TX09007, early-summer 44.4 2.85 36.5

 TX09007, mid-summer 46.2 1.95 33.3

 Graze All, early-summer 42.0 2.49 35.4

 Graze All, mid-summer 45.5 2.46 33.1

 Graze. n. Bale, early-summer 39.9 2.33 35.6

 Graze. n. Bale, early-summer 41.3 1.83 33.0

 LSD§4.15 0.527 20.39

ANOVA -----------------------P-values------------------------

 environment¶>0.99 0.2817 0.4285

 Type 0.002 0.0532 0.2024

 hybrid <.0001 <.0001 0.3691

 N rate <.0001 <.0001 0.9458

 type x N rate 0.4057 0.7051 0.9824

 N rate x hybrid 0.5058 0.9563 0.932

 time period 0.196 0.0666 0.6069

 type x time period 0.8334 <.0001 0.7202

 time period x hybrid 0.0925 0.0325 0.9821

 N rate x time period 0.3085 0.1908 0.6689

 type x Nrate x time period 0.8907 0.9649 0.7227

 N rate x time period x hybrid 0.6106 0.8876 0.8173

† LSD values for comparing within N rate.

‡ LSD values for comparing values within type x time period interaction.

§ LSD values for comparing values within time period x hybrid(type) interaction.

Differences greater than LSD values are significantly different at the α = 0.05 level.

¶ environment is a random effect and when its covariance parameter estimate was equal 

to zero it’s p-value (based upon a Wald Z statistic) was set to > 0.99.
125

Table 5.4. Harvest TN and harvest TC samples from Urbana 2009 and all 2010 environments (Urbana 2010, 

Dixon Springs 2010, and Perry 2010) showing results by N rate and for the type x N rate interaction. Analysis of 

variance (ANOVA) for each variable is shown at bottom of table. 

-------------------------Urbana 2009------------------------- ----------------------2010 environments---------------------

harvest TN harvest TC harvest TN harvest TC

N rate, kg N ha-1g kg-1 g kg-1 N rate, kg N ha-1g kg-1 g kg-1

 0 0.79 33.3 0 0.91 39.7

 50 0.80 32.3 56 0.97 39.9

 100 0.82 34.9 112 1.09 41.7

 150 0.99 34.1 168 1.04 40.7

 LSD†0.180 4.07 224 1.13 41.9

type x N rate LSD†

0.194 3.22

 energy, 0 0.76 31.6 type x N rate

 energy, 50 0.77 32.1 energy, 0 0.68 39.6

 energy, 100 0.67 34.2 energy, 56 0.67 39.4

 energy, 150 0.86 35.0 energy, 112 0.74 41.3

 forage, 0 0.81 35.1 energy, 168 0.73 40.2

 forage, 50 0.83 32.6 energy, 224 0.74 40.1

 forage, 100 0.96 35.5 forage, 0 1.14 39.8

 forage, 150 1.12 33.2 forage, 56 1.27 40.3

 LSD‡0.322 6.04 forage, 112 1.45 42.1

ANOVA --------P-values-------- forage, 168 1.35 41.1

 Type 0.1838 0.6199 forage, 224 1.52 43.7

 Hybrid 0.9024 0.9451 LSD‡0.604 4.73

 N rate 0.0876 0.6038 ANOVA --------P-values--------

 type x Nrate 0.4032 0.621 environment 0.4035 0.1641

 N rate x hybrid 0.8204 0.2918 type 0.0589 0.2007

 hybrid 0.5728 0.1358

 N rate 0.1389 0.505

 type x Nrate 0.1773 0.845

 N rate x hybrid 0.3918 0.5652

† LSD values for comparing within N rate.

‡ LSD values for comparing values of type x N rate interaction.

Differences greater than LSD values are significantly different at the α = 0.05 level.
126

FIGURES

Figure 5.1. Biomass yields (Mg DM ha-1) and residuals of energy and forage sorghum in each 

environment fit with quadratic plus plateau response curves and linear response curves where quadratic 

plus plateau response curves could not be fitted to the data. Both sorghum types at Urbana 2009 and 

energy sorghum at Perry 2010 were fitted with linear response curves, while the others were fit with 

quadratic plus plateau response curves. 
127

CONCLUDING REMARKS

When considering biomass production of switchgrass, M. x giganteus, and forage and 

energy sorghum, the effects of the environment and field management practices are very critical 

elements that can greatly affect biomass yield and productivity. Because these crops were not 

evaluated in the same experiment no direct comparisons about these crops can be discussed, 

although each individual study provides valuable information about these three crops. Future 

research could also benefit from a comparison of these crops in side-by-side experiments.

In Ch. 2, an evaluation of data from 106 sites and 45 studies from the eastern two thirds 

of the USA and southeastern Canada revealed that N rate, region, spring precipitation, growing 

season, ecotype, and harvest timing each had a significant effect on biomass yields of 

switchgrass. Results of this study also revealed that N response varies from site-to-site, that the 

lower and upper central USA regions tend to produce greater biomass yields than the northern or 

southern regions of the USA, and that the lowland ecotype tends to yield better than the upland 

ecotype, although this depends on the region in which it is grown. Several issues made the 

compilation of the database for this study and its statistical analysis challenging. First, 

management practices and methods of reporting biomass yields were different from study-tostudy and provided a challenge in classifying data into a discrete set of categories. This often 

resulted in the exclusion of valuable data. In several cases, data from certain studies were 

excluded from the database because rather than reporting biomass yield for specific management 

practices (that were of interest in this study) such as N rate and harvest timing, biomass yields 

were often reported as averages across these variables. A random coefficients model was 

implemented that allowed several variables of interest to be considered together in a single 

model. Because the number of variables of interest was relatively high, it was not possible to 
128

estimate the variance (i.e. standard error of the differences of means, confidence intervals, 

standard deviations, coefficient of variation) within and among studies in way that was consistent 

or standardized from study-to-study. Based upon the results of this study, there are several areas 

of that could benefit from future research, some of which are already in progress. First, 

continued development of new switchgrass cultivars with improved biomass yield potential and 

broad adaptation is necessary to provide reliable and high biomass yielding switchgrass crops. 

Secondly, one of the most important decisions a producer can make is selecting the proper 

ecotype and cultivar suited for biomass production in his or her area. For example, Cave-inRock is a cultivar that originates from southern IL. This cultivar may be a front runner candidate 

for biomass production in northern IL, but other cultivars such as Kanlow may possess greater 

biomass yield potential in southern and central IL. It is necessary that local scientists and 

agronomists evaluate potential cultivars for a specific area and develop recommendations that 

will help producers make informed cultivar selection decisions. 

In Ch. 3, M. x giganteus was grown for three years (establishment years) in four 

environments ranging from the western Corn Belt in Mead, NE to the Atlantic Coast in 

Adelphia, NJ. It was observed that late planting and cold winter temperatures may limit the 

production range for this clone. M. x giganteus biomass yields did not respond significantly to N 

application during this study, but according to the literature, response to N application in future 

production years (beyond establishment) is more likely. Biomass yields were greatly impacted 

by environment, with low yields associated with poor soil conditions and hot dry conditions, and 

high biomass yields associated with high levels of well distributed rainfall (throughout the 

growing season) and warm growing conditions as were observed in NE in 2010. One of the 

major limitations of this study was the limited number of sites that were established and the 
129

broad range of geography that was evaluated. This limitation was due to a lack of sufficient M. x

giganteus rhizome material available for establishing replicated experiments at more sites. Four 

areas of potential production were evaluated (NE, IL, KY, and NJ), but only one location in each 

of these areas was established with a replicated experiment. This made the evaluation of these 

specific production areas difficult, because in each year, production area was confounded with 

environment, due to a lack of replication within area. For example, it may have been better for 

IL if replicated experiments had been set up at six locations at the main University of Illinois 

research and extension centers (Urbana, Brownstown, Dixon Springs, Perry, Monmouth, and 

Dekalb). A further step would have been to set up multiple replicated experiments at or near 

each of these research and education centers so that different soil types and potentially different 

environment effects could have been observed. This would have provided a hierarchical 

experimental design which includes the effects of area, location within area, and site within 

location. Because there is still limited published research available on M. x giganteus biomass 

production in the USA, many more sites and years of production are needed to suggest an 

optimal production region and how it performs under different management practices such as 

harvest timing and response to fertilizer application. One specific management practice that 

needs further evaluation is the effect of N fertilization on fully productive stands of M. x

giganteus, or M. x giganteus stands that are at least 3 to 5 years old. 

In Ch. 4, forage and energy sorghum hybrids were grown in four IL environments under 

different N rates. It was observed that environment had a major impact on plant growth and total 

biomass yields, and in each environment forage sorghum harvested twice annually (summer and 

fall) yielded less than energy sorghum harvested once in the fall. Biomass yield, leaf area index, 

and plant height response to N rate were observed up to 150 kg N ha-1in 2009 and up to at least 
130

168 kg N ha-1in 2010. Photoperiod sensitivity allowed the energy sorghum hybrids to continue 

vegetative growth until late Sept. which allowed them to continue accumulating plant height and 

biomass throughout the majority of the growing season. Future research would benefit from 

more sites and more years of production. This study was limited to three locations, two years, 

and a total of four environments. Multiple years of production for a given site would provide

more useful information for calculating optimal N rates than a single year of production at that

site (Bullock and Bullock, 1994). If agronomists were to continue evaluating sorghum in IL for 

biomass production, a couple of research areas should be pursued. First, continue evaluating 

energy and forage sorghum hybrids under a wide range of N rates, and establish trials at several 

locations (i.e. six main research and extension centers in IL) for two or three or even more years. 

Of course, this should be done under soil conditions that have adequate levels phosphorous, 

potassium and other essential macro and micro nutrients. One major foreseen challenges in 

dealing with sorghum biomass is the high moisture content of the biomass material. Even when 

harvested in Nov., the biomass still had approximately 60% moisture. The process of baling and 

hauling this material to a conversion facility would require that this biomass be dried down to a 

level that would allow long term storage. Baled biomass with high moisture content, stored for 

even a short period of time, can result in rapid decomposition and even spontaneous combustion. 

Biomass harvesters with conditioners could potentially provide a means to crack and break apart 

the sorghum stems so that significant field drying would occur. Also, harvesting earlier in the 

season could provide sufficient time for the crop to dry in the field when there is still plenty of 

warm late summer and early fall weather. In each case, the biomass would likely need to be 

spread out evenly on the field (for drying purposes) or windrowed in a way that allows the 
131

biomass to be turned over with equipment such as a hay rake that can expose the underside of the 

windrow to the sun and air. 

REFERENCES

Bullock, D.S., and D.G. Bullock. 1994b. Calculation of optimal nitrogen fertilizer rates. Agron. 

J. 86: 921-923.
132

APPENDIX A

Selected SAS and R code for Ch. 2

Cultivar means, SEM, min, max-Ch.2, Table 2.4.

options ps=100 ls=100 nodate nocenter nonumber formdlim='*';

data SW;

length Cultivar$ 30;

infile 'C:\Documents and Settings\maughan2\My Documents\My Dropbox\Switchgrass Meta-Analysis\SW 

database June 18, 2011, no Europe, no Irrigation.csv'

dlm="," firstobs=2;

input PubName$ Category$ Publication$ Experiment sites$ Country$ Location$ Region$ Latitude 

Longitude 

soil$ establishment$ SeedRate$ Precip spring Irrigation$ year$ season$ Cultivar$ 

Ecotype$ Cuttings 

harvest$ Nrate biomass notes$;

if Nrate > 300 then delete;

cards;

run;

proc print; run;

data sw; set sw;

if cultivar="mean" then delete;

if cultivar="NA" then delete;

if cultivar="unknown" then delete;

if cultivar="average" then delete;

if cultivar="Mean" then delete;

if cultivar="average of 3 varieties" then delete;

*if cuttings=2 then delete;

if season=1 then delete;

*if season=2 then delete;

run; proc print; run;

proc sort data=sw; by season region cultivar; run;

proc means data=sw noprint mean std min max; var biomass; by season region cultivar;

output out=cultmean mean=meanbiomass std=stdbiomass min=minbiomass max=maxbiomass;

run; proc print data=cultmean; run;

Descriptive statistics, model development-Ch.2, Fig. 2.2, Fig. 2.3, Fig. 2.4, Fig. 2.5, Fig. 2.6, 

Table 2.5, Table 2.6 in part.

## Load packages

library(nlme) #library(help = nlme)

library(Hmisc)

library(lattice) #library(help = lattice)

library (graphics) #library(help = graphics)

#library(help = nlme)

## Load file

sw <- read.csv("./SW database June 18, 2011, no Europe, no Irrigation.csv")

head(sw)

sw.NR <-sw[sw$Region == "North",]

unique(sw.NR[,5])

sw.UCR <-sw[sw$Region == "Upper-Central",]

unique(sw.UCR[,5])

sw.LCR <-sw[sw$Region == "Lower-Central",]

unique(sw.LCR[,5])

sw.SR <-sw[sw$Region == "South",]

unique(sw.SR[,5])

unique(sw[,3]) #45 studies

unique(sw[,5]) #106 site or location combinations

sw #1476 observations

##which varieties are yielding greater than 26? Kanlow, Alamo.
133

sw[sw$biomass.Mg.ha.1. > 28,"variety"]

 ##which varieties are yielding greater than 30? Kanlow, Alamo.

sw[sw$biomass.Mg.ha.1. > 30,"variety"]

##which varieties are yielding greater than 35? Kanlow, Alamo.

sw[sw$biomass.Mg.ha.1. > 35,"variety"]

## Removing data with N rates higher than 300. 

##can cause lodging and most recommended N rates have been much lower. 

sw[sw$N.rate.kg.ha. > 300,23]

sw[sw$N.rate.kg.ha. > 300,23] <- NA

## Removing lowland upper north

#sw[sw$Region == "north" & sw$Ecotype == "L",19]

#sw[sw$Region == "north" & sw$Ecotype == "L",19] <- NA

## Get rid of SeedRate

sw <- sw[,-13]

## getting rid of Precip.annual.mm.

head(sw)

sw <- sw[,-13]

head(sw)

length(unique(sw$sites))

head(sw)

unique(sw$sites)

sw$sites <- factor(sw$sites, levels = unique(sw$sites))

## I will average over varieties before I do the analysis

## Averaged switchgrass biomass over varieties

sw.b <- with(sw,summarize(biomass.Mg.ha.1., 

 llist(sites, Region, Apr.June.precip..mm., season, Ecotype, Cuttings, 

 harvest.timing, N.rate.kg.ha.), 

 FUN=mean, data = sw))

nms <- names(sw.b)

nms[8:9] <- c("Nrate","biomass")

names(sw.b) <- nms

sw.b$sites <- as.factor(sw.b$sites)

sw.b$Region <- factor(sw.b$Region, 

 levels=c("South","Lower-Central","Upper-Central", "North"))

sw.b$season <- as.factor(sw.b$season)

sw.b$Ecotype <- as.factor(sw.b$Ecotype)

sw.b$Cuttings <-as.factor(sw.b$Cuttings)

sw.b$harvest.timing <-as.factor(sw.b$harvest.timing)

sw.b$Apr.June.precip..mm. <- as.numeric(sw.b$Apr.June.precip..mm.)

sw.b$biomass <- as.numeric(sw.b$biomass)

sw.b$Nrate <- as.numeric(sw.b$Nrate)

write.csv(sw.b,"./sw-meta June 18, 2011.csv",row.names=FALSE)

rm(list=ls())

sw.b <- read.csv("./sw-meta June 18, 2011.csv")

#manually removing observations from NA's associated with N rates higher than 

#280 if the above code is rerun, this will have to be done again. 

unique(sw.b$sites)

head(sw.b)

sw.b.NR <-sw.b[sw.b$Region == "North",]

sw.b.UCR <-sw.b[sw.b$Region == "Upper-Central",]

sw.b.LCR <-sw.b[sw.b$Region == "Lower-Central",]

sw.b.SR <-sw.b[sw.b$Region == "South",]

range(sw.b$biomass) #0 to 33.4

#season 2 yields by ecotype

mean(sw.b[sw.b$season == "2" & sw.b$Ecotype == "L","biomass"]) #11.1
134

sd(sw.b[sw.b$season == "2" & sw.b$Ecotype == "L","biomass"]) #6.1

mean(sw.b[sw.b$season == "2" & sw.b$Ecotype == "U","biomass"]) #6.7

sd(sw.b[sw.b$season == "2" & sw.b$Ecotype == "U","biomass"]) #3.2

#seasons >=3 yields by ecotype

mean(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L","biomass"]) #12.6

sd(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L","biomass"]) #5.4

mean(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U","biomass"]) #9.3

sd(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U","biomass"]) #4.3

#biomass yield ranges by region

range(sw.b[sw.b$Region == "South","biomass"]) #0 to 30.4

range(sw.b[sw.b$Region == "Lower-Central","biomass"]) #4.6 to 33.4

range(sw.b[sw.b$Region == "Upper-Central","biomass"]) #2.5 to 26

range(sw.b[sw.b$Region == "North","biomass"]) #1.1 to 14.5

mean(sw.b[sw.b$Region == "South" & sw.b$season != "1","biomass"]) #9.3

sd(sw.b[sw.b$Region == "South" & sw.b$season != "1","biomass"]) #5.7

mean(sw.b[sw.b$Region == "Lower-Central" & sw.b$season != "1","biomass"]) #13.4

sd(sw.b[sw.b$Region == "Lower-Central" & sw.b$season != "1","biomass"]) #4.5

mean(sw.b[sw.b$Region == "Upper-Central" & sw.b$season != "1","biomass"]) #10.1

sd(sw.b[sw.b$Region == "Upper-Central" & sw.b$season != "1","biomass"]) #4.3

mean(sw.b[sw.b$Region == "North" & sw.b$season != "1","biomass"]) #7.3

sd(sw.b[sw.b$Region == "North" & sw.b$season != "1","biomass"]) #3.1

 

#biomass yield ranges by season

range(sw.b[sw.b$season == "1","biomass"]) #2.2 to 12.6

range(sw.b[sw.b$season == "2","biomass"]) #1.1 to 33.4

range(sw.b[sw.b$season == ">=3","biomass"]) #0 to 30.4

#biomass yields by season

mean(sw.b[sw.b$season == "1","biomass"]) #6.6

 sd(sw.b[sw.b$season == "1","biomass"]) #3

mean(sw.b[sw.b$season == "2","biomass"]) #9.1

 sd(sw.b[sw.b$season == "2","biomass"]) #5.5

mean(sw.b[sw.b$season == ">=3","biomass"]) #10.9

 sd(sw.b[sw.b$season == ">=3","biomass"]) #5.2

 

#yield increases from season 2 to seasons >=3

mean(sw.b[sw.b$season == "2" & sw.b$Ecotype == "L","biomass"]) ## 11.1

mean(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L","biomass"]) ## 12.6

mean(sw.b[sw.b$season == "2" & sw.b$Ecotype == "U","biomass"]) ## 6.7

mean(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U","biomass"]) ## 9.3

median(sw.b[sw.b$season == "2" & sw.b$Ecotype == "L","biomass"]) ## 10.1

median(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "L","biomass"]) ## 12.8

median(sw.b[sw.b$season == "2" & sw.b$Ecotype == "U","biomass"]) ## 6.7

median(sw.b[sw.b$season == ">=3" & sw.b$Ecotype == "U","biomass"]) ## 9.1

## What are the mean values by region

mean(sw.b[sw.b$Region == "South" & sw.b$Ecotype == "L","biomass"]) 

 ## The mean for the south lowland is 10.3

mean(sw.b[sw.b$Region == "South" & sw.b$Ecotype == "U","biomass"]) 

 ## The mean for the south upland is 5.2

mean(sw.b[sw.b$Region == "Lower-Central" & sw.b$Ecotype == "L","biomass"]) 

 ## The mean for the lower central lowland is 14.4

mean(sw.b[sw.b$Region == "Lower-Central" & sw.b$Ecotype == "U","biomass"]) 

 ## The mean for the lower central upland is 11.9

mean(sw.b[sw.b$Region == "Upper-Central" & sw.b$Ecotype == "L","biomass"]) 

 ## The mean for the upper central lowland is 9.9

mean(sw.b[sw.b$Region == "Upper-Central" & sw.b$Ecotype == "U","biomass"]) 

 ## The mean for the upper central upland is 9.7

mean(sw.b[sw.b$Region == "North" & sw.b$Ecotype == "L","biomass"]) 

 ## The mean for the north lowland is 10.23

mean(sw.b[sw.b$Region == "North" & sw.b$Ecotype == "U","biomass"]) 

 ## The mean for the north upland is 7

## Removing lowland upper north, b/c few observation exist in the north region 

##for lowland ecotypes

sw.b[sw.b$Region == "North" & sw.b$Ecotype == "L",5]
135

sw.b[sw.b$Region == "North" & sw.b$Ecotype == "L",5] <- NA

unique(sw.b$Ecotype)

sw.b$Region <- factor(sw.b$Region, 

 levels = c("South", "Lower-Central", "Upper-Central", "North"))

bwplot(biomass ~ Region | Ecotype*season, data = sw.b, 

 xlab="Region",

 ylab="Dry Biomass Yield (Mg/ha)",

 fill=c("yellow"),

 panel = function(...) {

 panel.grid(v = 0, h=-1)

 panel.bwplot(...)

 })

## This suggests that yield is higher in the lower central region vs the

## upper central for lowland ecotypes but they are about the same for upland

## ecotypes

##boxplots by season

##FIGURE 3-PART 1

png("./manuscript figures/season1.png",width=600, height=1200, res=120)

sw.b.one <-sw.b[sw.b$season == "1",]

print(bwplot(biomass ~ Region | Ecotype, data = sw.b.one,

 xlab="Region", 

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")"))), 

 ylim=c(0,35),

 main = "growing season 1",

 fill=c("ivory2"),

 panel = function(...) {

 panel.grid(v = 0, h=-1)

 panel.bwplot(...)

 },

 layout = c(1,2)))

 dev.off()

 

 

##FIGURE 3-PART 2

png("./manuscript figures/season2.png",width=600, height=1200, res=120)

sw.b.two <-sw.b[sw.b$season == "2",]

print(bwplot(biomass ~ Region | Ecotype, data = sw.b.two,

 xlab="Region", 

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")"))), 

 ylim=c(0,35),

 main = "growing season 2",

 fill=c("ivory3"),

 panel = function(...) {

 panel.grid(v = 0, h=-1)

 panel.bwplot(...)

 },

 layout = c(1,2)))

 dev.off()

mean(sw.b.two[sw.b.two$Region == "South" & sw.b.two$Ecotype == "L","biomass"]) 

 ## The mean for the south lowland is 9.3

mean(sw.b.two[sw.b.two$Region == "South" & sw.b.two$Ecotype == "U","biomass"])

 ## The mean for the south upland is 5.5

mean(sw.b.two[sw.b.two$Region == "Lower-Central" & sw.b.two$Ecotype == "L","biomass"]) 

 ## The mean for the lower central lowland is 14.3

median(sw.b.two[sw.b.two$Region == "Lower-Central" & sw.b.two$Ecotype == "L","biomass"]) 

 ## The median for the lower central lowland is 10.3

mean(sw.b.two[sw.b.two$Region == "Lower-Central" & sw.b.two$Ecotype == "U","biomass"]) 

 ## The mean for the lower central upland is 12.9

mean(sw.b.two[sw.b.two$Region == "Upper-Central" & sw.b.two$Ecotype == "L","biomass"]) 

 ## The mean for the upper central lowland is 11.1

median(sw.b.two[sw.b.two$Region == "Upper-Central" & sw.b.two$Ecotype == "L","biomass"]) 

 ## The mean for the upper central lowland is 11.2

mean(sw.b.two[sw.b.two$Region == "Upper-Central" & sw.b.two$Ecotype == "U","biomass"]) 

 ## The mean for the upper central upland is 8.2

mean(sw.b.two[sw.b.two$Region == "North" & sw.b.two$Ecotype == "L","biomass"]) 
136

 ## The mean for the north lowland is NA

mean(sw.b.two[sw.b.two$Region == "North" & sw.b.two$Ecotype == "U","biomass"]) 

 ## The mean for the north upland is NA 

##FIGURE 3-PART 3

png("./manuscript figures/season3.png",width=600, height=1200, res=120) 

sw.b.three <-sw.b[sw.b$season == ">=3",]

print(bwplot(biomass ~ Region | Ecotype, data = sw.b.three,

 xlab="Region", 

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")"))),

 ylim=c(0,35),

 main = "growing seasons >=3",

 fill=c("ivory4"),

 panel = function(...) {

 panel.grid(v = 0, h=-1)

 panel.bwplot(...)

 },

 layout = c(1,2)))

 dev.off()

mean(sw.b.three[sw.b.three$Region == "South" & sw.b.three$Ecotype == "L","biomass"]) 

 ## 10.8

mean(sw.b.three[sw.b.three$Region == "South" & sw.b.three$Ecotype == "U","biomass"]) 

 ## 5.1

mean(sw.b.three[sw.b.three$Region == "Lower-Central" & sw.b.three$Ecotype == "L","biomass"]) 

 ## 14.6

median(sw.b.three[sw.b.three$Region == "Lower-Central" & sw.b.three$Ecotype == "L","biomass"]) 

 ## 14.6

mean(sw.b.three[sw.b.three$Region == "Lower-Central" & sw.b.three$Ecotype == "U","biomass"]) 

 ## 11.9

mean(sw.b.three[sw.b.three$Region == "Upper-Central" & sw.b.three$Ecotype == "L","biomass"]) 

 ## 10.4

median(sw.b.three[sw.b.three$Region == "Upper-Central" & sw.b.three$Ecotype == "L","biomass"]) 

 ## 10.7

mean(sw.b.three[sw.b.three$Region == "Upper-Central" & sw.b.three$Ecotype == "U","biomass"]) 

 ## 10

mean(sw.b.three[sw.b.three$Region == "North" & sw.b.three$Ecotype == "L","biomass"]) ## NA

mean(sw.b.three[sw.b.three$Region == "North" & sw.b.three$Ecotype == "U","biomass"]) ## NA 

library(latticeExtra)

sw.b$harvest.timing <-factor(sw.b$harvest.timing, levels = c("PSC", "ES"))

png("./manuscript figures/harvesttiming-ellipse.png",width=1500, height=600, res=120)

xyplot(biomass ~Nrate |factor(Region), groups=harvest.timing, data = sw.b,

 xlab=list(expression(paste("N rate ","(kg ",ha^-1,")")), cex=1.2),

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")")), cex=1.2),

 col=c("Green","Brown"), layout=c(4,1), auto.key=TRUE,

 panel = function(x,y,...){

 panel.xyplot(x,y, type = c("p"), pch=c(15,25), fill="transparent", 

 cex=1.2, ...)

panel.ellipse(x, y, lwd = 2, lty=c(1,2), center.pch=NULL, level=0.80, ...)

 }, 

 key=list(text=list(c("PSC","ES")), x=0.8, y=.9, cex.title=1.2, pch=c(15,25),

 points=TRUE, cex=1.2, lty=c(1, 2), lwd=2, lines=TRUE, 

 col=c("Green","Brown")))

dev.off()

 

#yield increases from season 2 to seasons >=3

mean(sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "PSC","biomass"]) ## 11.1

sd(sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "PSC","biomass"]) ## 6.3

mean(sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "ES","biomass"]) ## 8.2

sd(sw.b[sw.b$Region == "South" & sw.b$harvest.timing == "ES","biomass"]) ## 5

mean(sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "PSC","biomass"]) ## 6.4

sd(sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "PSC","biomass"]) ## 3

mean(sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "ES","biomass"]) ## 7.8

sd(sw.b[sw.b$Region == "North" & sw.b$harvest.timing == "ES","biomass"]) ## 3.2

##it appears that in the south, PSC yields better than ES, but in the North it is 

##the other way around. This may have to do with early harvesting injuring the crop 
137

##long term in the north, being unprepared for winter due to early harvest. 

##The south appears to have some recovery time due to lack of temperatures excessively 

##below freezing

##precipitation

png("./manuscript figures/spring precip.png",width=1200, height=600, res=120)

xyplot(biomass ~ Apr.June.precip..mm., group=Ecotype, data = sw.b,

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")")), cex=1.2),

 xlab = list("April-June Precipitation (mm)", cex=1.2),

 pch=c(1,17), col=c("black", "grey"), fill="transparent", lty=c(2,1), lwd = 2,

 type = c("p", "r"), cex=1,

 xlim=c(50,1020),

 key=list(x=0.5, y=1, cex.title=1.2,

 pch=c(1,17), cex=1.2,

 lty=c(2,1), lwd=2, points=TRUE, lines=TRUE,

 col=c("black", "grey"),

 text=list(c("Lowland", "Upland"))))

 dev.off()

#biomass yields tend to be higher with greater amounts of early season precipitation

#lowland yield better than upland

sw.b[sw.b$biomass > 34,]

#sw.b[sw.b$biomass > 34,10] <-NA

sw.b.n <- na.omit(sw.b)

sw.b.nG <- groupedData(biomass ~ Nrate | sites, data = sw.b.n)

unique(sw.b.n$sites)

xyplot(biomass ~ Nrate | sites, type = c("p","a"), data = sw.b.n)

##MODEL BUILDING

## model without random Nrate effects

quad.lme <- lme(biomass ~ Nrate + I(Nrate^2) ,

 random = ~ 1 | sites, data = sw.b.nG,

 na.action="na.omit")

quad.lme

anova(quad.lme)

plot(ranef(quad.lme))

plot(intervals(quad.lme))

## model with random effect for the quadratic term

fitquad.lme <- lme(biomass ~ Nrate + I(Nrate^2) ,

 random = ~ 1 + Nrate + I(Nrate^2) | sites, data = sw.b.nG,

 na.action="na.omit")

fitquad.lme

anova(fitquad.lme)

plot(ranef(fitquad.lme))

plot(intervals(fitquad.lme))

## A simpler model does not have random effect for the quadratic term

fitsimple.lme <- lme(biomass ~ Nrate + I(Nrate^2) ,

 random = ~ 1 + Nrate | sites, data = sw.b.nG,

 na.action="na.omit")

fitsimple.lme

anova(quad.lme,fitquad.lme,fitsimple.lme)

# Model df AIC BIC logLik Test L.Ratio p-value

#quad.lme 1 5 3490.786 3512.788 -1740.393 

#fitquad.lme 2 10 3485.885 3529.888 -1732.943 1 vs 2 14.901160 0.0108

#fitsimple.lme 3 7 3480.126 3510.928 -1733.063 2 vs 3 0.241361 0.9707

anova(fitsimple.lme)

plot(ranef(fitsimple.lme))

## The simpler model is better, rather parsimony is better here. 
138

coef(fitsimple.lme)

plot(augPred(fitsimple.lme, level=0:1, data=sw.b.nG))

##NOW WE ADD IN VARIABLES and use a backward elimination method. IF p-value larger 

##than 0.1 then drop. adding all main effects and suspected interactions that 

##could have valid interpretations, based upon literature and xyplots.

fit.lme <- lme(biomass ~ Nrate +

 I(Nrate^2) +

 Region +

 Apr.June.precip..mm. +

 season +

 Ecotype +

 harvest.timing +

 Cuttings +

 Apr.June.precip..mm.:Region +

 Apr.June.precip..mm.:Ecotype +

 Apr.June.precip..mm.:season +

 Nrate:Ecotype +

 I(Nrate^2):Ecotype +

 Region:harvest.timing + 

 season:Ecotype, 

 random = ~ 1 + Nrate | sites,

 data = sw.b.nG)

 

#other potential interacations that result in singularity or don't makes sense.

#Region:Ecotype clearly is significant in reality but it is suspected that the 

##unbalanced nature of the data render this interaction a singularity problem.

#harvest.timing:Cuttings interaction comes out significant if left in, but I 

##don't think there is a logical explanation for it, and Cuttings alone is non significant. 

##I just tried this, but don't think it is logical. 

anova(fit.lme) 

# numDF denDF F-value p-value

#(Intercept) 1 487 918.5267 <.0001

#Nrate 1 487 34.8070 <.0001

#I(Nrate^2) 1 487 4.3686 0.0371

#Region 3 93 16.7507 <.0001

#Apr.June.precip..mm. 1 487 14.7192 0.0001

#season 2 487 3.1355 0.0444

#Ecotype 1 487 104.6983 <.0001

#harvest.timing 1 487 3.1562 0.0763

#Cuttings 1 487 0.1949 0.6591

#Region:Apr.June.precip..mm. 3 487 1.5727 0.1951

#Apr.June.precip..mm.:Ecotype 1 487 8.7157 0.0033

#Apr.June.precip..mm.:season 2 487 7.6962 0.0005

#Nrate:Ecotype 1 487 3.3621 0.0673

#I(Nrate^2):Ecotype 1 487 0.2175 0.6411

#Region:harvest.timing 3 487 2.2997 0.0766

#season:Ecotype 2 487 0.4422 0.6429

#now removing non-significant terms

fit2.lme <- lme(biomass ~ Nrate +

 I(Nrate^2) +

 Region +

 Apr.June.precip..mm. +

 season +

 Ecotype +

 harvest.timing +

 Apr.June.precip..mm.:Ecotype +

 Apr.June.precip..mm.:season +

 Nrate:Ecotype +

 Region:harvest.timing, 

 random = ~ 1 + Nrate | sites,

 data = sw.b.nG)

anova(fit2.lme)
139

# numDF denDF F-value p-value

#(Intercept) 1 494 899.7931 <.0001

#Nrate 1 494 34.9935 <.0001

#I(Nrate^2) 1 494 4.4053 0.0363

#Region 3 93 16.2564 <.0001

#Apr.June.precip..mm. 1 494 14.7079 0.0001

#season 2 494 3.0845 0.0466

#Ecotype 1 494 104.9683 <.0001

#harvest.timing 1 494 3.1382 0.0771

#Apr.June.precip..mm.:Ecotype 1 494 6.8370 0.0092

#Apr.June.precip..mm.:season 2 494 7.4240 0.0007

#Nrate:Ecotype 1 494 3.4461 0.0640

#Region:harvest.timing 3 494 2.2497 0.0817

coef(fit2.lme)

summary(fit2.lme)

fixed.effects(fit2.lme)

intervals(fit2.lme)

 

## Spring precipitation seems to interact strongly with season, this means

## that biomass responds differently to precipitation depending on the

## season. As the crop matures from season 1 to season 2 and seasons >=3, 

## similar precipitation levels result in higher biomass yields

#also spring precipitation interacts with ecotype, and this makes since since lowland ecotypes 

#tend to come from wetter environments than upland ecotypes, and likely do better under wetter 

#conditions than upland ecotypes. 

#Lowland and upland ecotypes may have slightly different N requirements. This has been suggested 

#in the old literature 

##in the south, PSC harvests tend to result in greater biomass yields, but not so in the north. 

##In the north, PSC harvest likely injured the stands by removing nutrients needed for winter 

##survival, causing yields to decline over time. This is just one possible explanation for 

##this interaction

plot(fit2.lme)

qqnorm(fit2.lme, abline=c(0,1))

## final model

unique(sw.b.nG$sites)

## Sort the sites

sw.b.nG$sites <- factor(sw.b.nG$sites, levels = unique(sw.b.nG$sites))

unique(sw.b.nG$sites)

augpred <- augPred(fit2.lme, level=0:1, group=Region, data=sw.b.nG)

augpred$.groups <- factor(augpred$.groups, levels = unique(sw.b.nG$sites))

png("./manuscript figures/site_predictions.png", width=1200, height=1600, res=140)

plot(augpred,

 xlab=list(expression(paste("N rate ","(kg ",ha^-1,")")), cex=1.2),

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")")), cex=1.2),

 as.table = TRUE,

 layout = c(8,13),

 lty=c(1,2),col="black",

 key=list(text=list(c("fixed","experimental unit")),lty=c(1,2),lines=T))

dev.off()

#plotting predictions 300mm SPRING PRECIPITATION, PSC, growing seasons >=3

rgn <- c("South","Lower-Central","Upper-Central","North")

nd <- expand.grid(Region = rgn, season = ">=3", Nrate = c(0,50,100,150,200,250), 

 Ecotype = c("L","U"), Apr.June.precip..mm.=300,

 harvest.timing ="PSC")

pp <- predict(fit2.lme, newdata = nd, level = 0)

preds <- data.frame(pp=pp, nd)

preds$type <- "predicted"

names(preds) <- c("biomass", "Region", "season", "Nrate" , "Ecotype", "Precip", 

 "harvest.timing", "type")

sw.psc <-sw.b.nG[sw.b.nG$harvest.timing == "PSC" & sw.b.nG$season == ">=3",]

# sw.b.nG$Apr.June.precip..mm. >=200 & sw.b.nG$Apr.June.precip..mm. <=400,]
140

 

obs <- with(sw.psc, data.frame(biomass = biomass, Region = Region, season = season, 

 Nrate = Nrate, Ecotype = Ecotype, 

 Precip = Apr.June.precip..mm., harvest.timing = harvest.timing, type = "observed"))

pred.obs <- rbind(preds, obs)

pred.obs$Region <- factor(pred.obs$Region, 

 levels=c("South","Lower-Central","Upper-Central","North"))

pred.obs$type <- factor(pred.obs$type)

pred.obs$type.Ecotype <- with(pred.obs, type:Ecotype)

pred.obs[pred.obs$type == 'predicted' & pred.obs$Region == 'North'

 & pred.obs$Ecotype == 'L', 'biomass'] <- NA

png('./manuscript figures/predicted-and-observed-PSC-300mm.png', width=1500, height=600, res=120)

xyplot(biomass ~ Nrate | Region,

 data = pred.obs, main="growing seasons >=3, PSC harvest, 300mm spring precipitation", 

 cex.title=1.2,

 pch = c(1,17), fill="transparent",

 cex=1.2,

 groups = type.Ecotype,

 layout = c(4,1),

 xlab=list(expression(paste("N rate ","(kg ",ha^-1,")")), cex=1.2),

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")")), cex=1.2),

 col = c("black", "grey"),

 ylim=c(0,35),

 panel = function(x,y,...){

 panel.superpose.2(x,y,type=c('p','p','l','l','l','l'), lty=c(2,1), lwd = 2,...)

 },

 key = list(x=0.8, y=.9, cex.title=1.2,

 pch=c(1,17), cex=1.2,

 lty=c(2,1), lwd=2, points=TRUE, lines=TRUE,

 col=c("black", "grey"),

 text=list(c("Lowland", "Upland"))))

dev.off()

#plotting predictions 300mm SPRING PRECIPITATION, ES, growing seasons >=3

rgn <- c("South","Lower-Central","Upper-Central","North")

nd <- expand.grid(Region = rgn, season = ">=3", Nrate = c(0,50,100,150,200,250), 

 Ecotype = c("L","U"), 

 Apr.June.precip..mm.=300,

 harvest.timing ="ES")

pp <- predict(fit2.lme, newdata = nd, level = 0)

preds <- data.frame(pp=pp, nd)

preds$type <- "predicted"

names(preds) <- c("biomass", "Region", "season", "Nrate" , "Ecotype", "Precip", 

 "harvest.timing", "type")

sw.es <-sw.b.nG[sw.b.nG$harvest.timing == "ES" & sw.b.nG$season == ">=3",]

# & sw.b.nG$Apr.June.precip..mm. >=200 & sw.b.nG$Apr.June.precip..mm. <=400,]

 

obs <- with(sw.es, data.frame(biomass = biomass, Region = Region, season = season, 

 Nrate = Nrate, Ecotype = Ecotype, 

 Precip = Apr.June.precip..mm., harvest.timing = harvest.timing, type = "observed"))

pred.obs <- rbind(preds, obs)

pred.obs$Region <- factor(pred.obs$Region, 

 levels=c("South","Lower-Central","Upper-Central","North"))

pred.obs$type <- factor(pred.obs$type)

pred.obs$type.Ecotype <- with(pred.obs, type:Ecotype)

pred.obs[pred.obs$type == 'predicted' & pred.obs$Region == 'North'
141

 & pred.obs$Ecotype == 'L', 'biomass'] <- NA

png('./manuscript figures/predicted-and-observed-ES-300mm.png', width=1500, height=600, res=120)

xyplot(biomass ~ Nrate | Region,

 data = pred.obs, main="growing seasons >=3, ES harvest, 300mm spring precipitation", 

cex.title=1.2,

 pch = c(1,17), fill="transparent",

 cex=1.2,

 groups = type.Ecotype,

 layout = c(4,1),

 xlab=list(expression(paste("N rate ","(kg ",ha^-1,")")), cex=1.2),

 ylab=list(expression(paste("Dry biomass yield ","(Mg ",ha^-1,")")), cex=1.2),

 col = c("black", "grey"),

 ylim=c(0,35),

 panel = function(x,y,...){

 panel.superpose.2(x,y,type=c('p','p','l','l','l','l'), lty=c(2,1), lwd = 2,...)

 },

 key = list(x=0.8, y=.9, cex.title=1.2,

 pch=c(1,17), cex=1.2,

 lty=c(2,1), lwd=2, points=TRUE, lines=TRUE,

 col=c("black", "grey"),

 text=list(c("Lowland", "Upland"))))

dev.off()

##removing outliers for supplementary results

## beginnning with final model 

fit2.lme <- lme(biomass ~ Nrate +

 I(Nrate^2) +

 Region +

 Apr.June.precip..mm. +

 season +

 Ecotype +

 harvest.timing +

 Apr.June.precip..mm.:Ecotype +

 Apr.June.precip..mm.:season +

 Nrate:Ecotype +

 Region:harvest.timing, 

 random = ~ 1 + Nrate | sites,

 data = sw.b.nG)

anova(fit2.lme)

# numDF denDF F-value p-value

#(Intercept) 1 494 899.7931 <.0001

#Nrate 1 494 34.9935 <.0001

#I(Nrate^2) 1 494 4.4053 0.0363

#Region 3 93 16.2564 <.0001

#Apr.June.precip..mm. 1 494 14.7079 0.0001

#season 2 494 3.0845 0.0466

#Ecotype 1 494 104.9683 <.0001

#harvest.timing 1 494 3.1382 0.0771

#Apr.June.precip..mm.:Ecotype 1 494 6.8370 0.0092

#Apr.June.precip..mm.:season 2 494 7.4240 0.0007

#Nrate:Ecotype 1 494 3.4461 0.0640

#Region:harvest.timing 3 494 2.2497 0.0817

plot(fit2.lme) ##residuals look OK, some outliers

## checking the residuals for outliers

rnm <- row.names(na.omit(sw.b.nG[,-3]))

plot(fit2.lme, id = 0.08, idLabels = rnm)

qqnorm(fit2.lme, abline=c(0,1))

## Outliers 233, 387, 29, 617, 235

sw.b.nmd <- na.omit(sw.b.nG)
142

sw.b.n2 <- sw.b.nmd[!row.names(sw.b.nmd) %in% c(233, 387, 29, 617, 235),]

## What are these outliers?

unique(sw.b.nmd[!row.names(sw.b.nmd) %in% c(233, 387, 29, 617, 235),]$sites)

sw.b.nmd[row.names(sw.b.nmd) %in% c(233, 387, 29, 617, 235),]

# sites Region Apr.June.precip..mm. season Ecotype Cuttings harvest.timing Nrate biomass

#29 10a South 393 >=3 L 1 ES 150 24.36441

#233 24a Lower-Central 379 2 L 2 ES 0 33.40000

#235 24a Lower-Central 379 2 L 2 ES 224 29.50000

#387 40a South 243 >=3 L 2 ES 84 28.63500

#617 72a Lower-Central 475 >=3 L 2 ES 67 25.97100

fit2b.lme <- lme(biomass ~ Nrate +

 I(Nrate^2) +

 Region +

 Apr.June.precip..mm. +

 season +

 Ecotype +

 harvest.timing +

 Apr.June.precip..mm.:Ecotype +

 Apr.June.precip..mm.:season +

 Nrate:Ecotype +

 Region:harvest.timing, 

 random = ~ 1 + Nrate | sites,

 data = sw.b.n2)

anova(fit2b.lme)

# numDF denDF F-value p-value

#(Intercept) 1 489 938.0432 <.0001

#Nrate 1 489 38.0413 <.0001

#I(Nrate^2) 1 489 7.6106 0.0060

#Region 3 93 17.2381 <.0001

#Apr.June.precip..mm. 1 489 18.0897 <.0001

#season 2 489 3.8675 0.0216

#Ecotype 1 489 110.0736 <.0001

#harvest.timing 1 489 3.8717 0.0497

#Apr.June.precip..mm.:Ecotype 1 489 5.9813 0.0148

#Apr.June.precip..mm.:season 2 489 8.7673 0.0002

#Nrate:Ecotype 1 489 4.7955 0.0290

#Region:harvest.timing 3 489 2.5163 0.0576

 

plot(fit2b.lme) ##residuals look pretty good

 

plot(augPred(fit2b.lme, level=0:1))

qqnorm(fit2b.lme, abline=c(0,1))
143

APPENDIX B

Selected SAS and R code for Ch. 3

Plant height-Ch.3, Table 3.6, Fig. 3.1

library(nlme)

library(Hmisc)

library(lattice)

library (graphics)

#2009 & 2010 ht data

grwth <- read.csv(file = "../2009 2010 ht data.csv", skip = 2, na.strings=".")

head(grwth)

grwth

grwth <- grwth[-373:-426,] #getting rid of Illinois

head(grwth)

unique(grwth$Year)

grwth$Year <- as.factor(grwth$Year)

head(grwth)

xyplot(grwth[,8] ~ grwth[,10],

 ylab = "Height",

 xlab = "day of year",

 groups = grwth[,1],

 auto.key=TRUE)

 

grwth2 <- data.frame(env=grwth[,11],

 doy = grwth[,10],

Nrate = grwth[,6],

height = grwth[,8],

TT = grwth[,9])

 

## developing a model

expu <- with(grwth, Environment:factor(Rep):factor(N.rate.kg.ha))

grwthG <- groupedData(height ~ doy | expu, data = grwth2)

head(grwthG)

fit.nls <- nlsList(height ~ SSlogis(doy, Asym, xmid, scal), data = grwthG)

fit.nlme <- nlme(fit.nls)

anova(fit.nlme)

plot(augPred(fit.nlme, level = 0:1))

fix2 <- fixef(fit.nlme)

fix2

fit2.nlme <- update(fit.nlme, fixed = Asym + xmid + scal ~ Nrate,

 start = c(fix2[1], 0, fix2[2], 0, fix2[3], 0))

anova(fit2.nlme)

# numDF denDF F-value p-value

#Asym.(Intercept) 1 379 6453.716 <.0001

#Asym.Nrate 1 379 0.001 0.9725

#xmid.(Intercept) 1 379 14449.984 <.0001

#xmid.Nrate 1 379 0.001 0.9717

#scal.(Intercept) 1 379 2782.442 <.0001

#scal.Nrate 1 379 0.155 0.6940

## Add the effect of env

fix3 <- fixef(fit2.nlme)

fix3

fit3.nlme <- update(fit.nlme, fixed = Asym + xmid + scal ~ Nrate + env,

 start = c(fix3[1:2], 0, 0, 0, 0, 0, fix3[3:4], 0, 0, 0, 0, 0, fix3[5:6], 0, 0, 0, 0, 0))

anova(fit3.nlme)
144

## Add the interaction of Nrate*loc

fix4 <- fixef(fit3.nlme)

fix4

fit4.nlme <- update(fit3.nlme, fixed = Asym + xmid + scal ~ Nrate * env,

 start = c(fix4[1:7], 0, 0, 0, 0, 0, fix4[8:14], 0, 0, 0, 0, 0, fix4[15:21], 0, 0, 0, 0, 0))

anova(fit4.nlme)

# numDF denDF F-value p-value

#Asym.(Intercept) 1 349 14528.41 <.0001

#Asym.Nrate 1 349 1.21 0.2713

#Asym.env 5 349 1039.42 <.0001

#Asym.Nrate:env 5 349 3.08 0.0099

#xmid.(Intercept) 1 349 167106.08 <.0001

#xmid.Nrate 1 349 0.02 0.8942

#xmid.env 5 349 96.82 <.0001

#xmid.Nrate:env 5 349 0.74 0.5913

#scal.(Intercept) 1 349 3552.38 <.0001

#scal.Nrate 1 349 0.40 0.5276

#scal.env 5 349 11.61 <.0001

#scal.Nrate:env 5 349 0.25 0.9382

## Simplify error structure

##looking for more negative values of BIC

fit5.nlme <- update(fit4.nlme, random = pdDiag(list(Asym + xmid + scal ~ 1)))

fit5.nlme #the std. deviations for xmid and scale are very small here and could be eliminated

anova(fit4.nlme, fit5.nlme)

## Simpler model is better

fit6.nlme <- update(fit5.nlme, random = pdDiag(Asym ~ 1))

anova(fit5.nlme, fit6.nlme)

#this simplification is best

fit7.nlme <- update(fit6.nlme, weights = varPower())

fit7.nlme

anova(fit6.nlme, fit7.nlme)

# Model df AIC BIC logLik Test L.Ratio p-value

#fit6.nlme 1 38 -422.4554 -265.8007 249.2277 

#fit7.nlme 2 39 -420.4606 -259.6834 249.2303 1 vs 2 0.005209665 0.9425

#does not seem to be any better since BIC values are not more negative for fit7.nlme

#I will stick with fit6.nlme

## Investigating the effect, especially the interaction, now that our error structure is settled?

anova(fit6.nlme)

# numDF denDF F-value p-value

#Asym.(Intercept) 1 349 7945.70 <.0001

#Asym.Nrate 1 349 0.60 0.4392

#Asym.env 5 349 827.98 <.0001

#Asym.Nrate:env 5 349 1.74 0.1249

#xmid.(Intercept) 1 349 168312.32 <.0001

#xmid.Nrate 1 349 0.02 0.8832

#xmid.env 5 349 97.36 <.0001

#xmid.Nrate:env 5 349 0.74 0.5954

#scal.(Intercept) 1 349 3537.96 <.0001

#scal.Nrate 1 349 0.41 0.5247

#scal.env 5 349 11.60 <.0001

#scal.Nrate:env 5 349 0.26 0.9369

#the interaction is only somewhat sifnificant for the asymptote, 

#the effect of N is inconsistent from location to location, and appears generally to not be 

important
145

#I will reduce down to just Nrate + env 

 

fit8.nlme <- update(fit6.nlme, fixed = Asym + xmid + scal ~ Nrate + env, 

 start = c(fix3[1:2], 0, 0, 0, 0, 0, fix3[3:4], 0, 0, 0, 0, 0, fix3[5:6], 0, 0, 0, 0, 0))

plot(fit8.nlme)

qqnorm(fit8.nlme, abline=c(0,1))

#residuals look are not too bad

fit8.ss <- summary(fit8.nlme)

fit8.ss$tTable

# Value Std.Error DF t-value p-value

#Asym.(Intercept) 3.079769e+00 0.0491411709 364 62.67186833 4.178050e-197

#Asym.Nrate -1.515855e-05 0.0002937831 364 -0.05159775 9.588775e-01

#Asym.envKY-2010 3.110370e-01 0.0569491855 364 5.46165884 8.755655e-08

#Asym.envNE-2009 -2.336358e-01 0.0698799919 364 -3.34338555 9.136056e-04

#Asym.envNE-2010 7.111425e-01 0.0558585500 364 12.73113115 5.792894e-31

#Asym.envNJ-2009 2.901095e-01 0.0574492388 364 5.04984017 7.005466e-07

#Asym.envNJ-2010 -7.862059e-02 0.0550161745 364 -1.42904501 1.538489e-01

#xmid.(Intercept) 1.514565e+02 1.2454308733 364 121.60972985 7.878221e-297

#xmid.Nrate -2.097660e-03 0.0075318111 364 -0.27850673 7.807815e-01

#xmid.envKY-2010 -7.984292e+00 1.4510276017 364 -5.50250872 7.073680e-08

#xmid.envNE-2009 2.000777e+01 1.9235455543 364 10.40150308 2.334178e-22

#xmid.envNE-2010 1.028235e+01 1.3937302031 364 7.37757353 1.100905e-12

#xmid.envNJ-2009 8.788309e+00 1.4479067746 364 6.06966470 3.220649e-09

#xmid.envNJ-2010 -4.613128e+00 1.3988466305 364 -3.29780847 1.070451e-03

#scal.(Intercept) 2.174922e+01 1.0618779263 364 20.48184901 1.446951e-62

#scal.Nrate 9.255571e-04 0.0067791685 364 0.13652959 8.914781e-01

#scal.envKY-2010 -8.905391e-01 1.2483827914 364 -0.71335423 4.760837e-01

#scal.envNE-2009 2.477236e+00 1.6164154659 364 1.53254920 1.262556e-01

#scal.envNE-2010 8.951886e-02 1.1837697880 364 0.07562185 9.397615e-01

#scal.envNJ-2009 -1.927412e+00 1.2862738772 364 -1.49844575 1.348842e-01

#scal.envNJ-2010 -6.366202e+00 1.2409978986 364 -5.12990539 4.723678e-07

intervals(fit8.ss, level=0.95, which="fixed")

# lower est. upper

#Asym.(Intercept) 2.985384e+00 3.079769e+00 3.174153826

#Asym.Nrate -5.794241e-04 -1.515855e-05 0.000549107

#Asym.envKY-2010 2.016554e-01 3.110370e-01 0.420418615

#Asym.envNE-2009 -3.678534e-01 -2.336358e-01 -0.099418124

#Asym.envNE-2010 6.038557e-01 7.111425e-01 0.818429349

#Asym.envNJ-2009 1.797674e-01 2.901095e-01 0.400451512

#Asym.envNJ-2010 -1.842895e-01 -7.862059e-02 0.027048292

#xmid.(Intercept) 1.490644e+02 1.514565e+02 153.848595643

#xmid.Nrate -1.656392e-02 -2.097660e-03 0.012368596

#xmid.envKY-2010 -1.077126e+01 -7.984292e+00 -5.197321360

#xmid.envNE-2009 1.631323e+01 2.000777e+01 23.702295046

#xmid.envNE-2010 7.605427e+00 1.028235e+01 12.959267334

#xmid.envNJ-2009 6.007332e+00 8.788309e+00 11.569285179

#xmid.envNJ-2010 -7.299876e+00 -4.613128e+00 -1.926380928

#scal.(Intercept) 1.970969e+01 2.174922e+01 23.788759082

#scal.Nrate -1.209511e-02 9.255571e-04 0.013946222

#scal.envKY-2010 -3.288292e+00 -8.905391e-01 1.507214158

#scal.envNE-2009 -6.273929e-01 2.477236e+00 5.581865317

#scal.envNE-2010 -2.184133e+00 8.951886e-02 2.363170778

#scal.envNJ-2009 -4.397942e+00 -1.927412e+00 0.543118617

#scal.envNJ-2010 -8.749771e+00 -6.366202e+00 -3.982632569

unique(grwth2$env)

grwth2$env <- factor(grwth2$env, 

 levels=c("NJ-2009", "NJ-2010", "NE-2009", "NE-2010", "KY-2009", "KY-2010"), order=TRUE)

unique(grwth2$env)

newdat <- expand.grid(Nrate = unique(grwth2$Nrate), env = unique(grwth2$env), doy = 50:290)

preds <- predict(fit6.nlme , newdata = newdat, level = 0)
146

prds <- cbind(newdat, preds)

obs <- with(grwthG, data.frame(Nrate = Nrate, env = env, doy = doy, height = height))

pred.obs <- merge(prds, obs, by=c("Nrate", "env", "doy"),all=TRUE)

 

## Do one plot at a time and then combine them

## Kentucky 2009

obs.KY.2009 <- obs[obs$env == 'KY-2009',]

prds.KY.2009 <- prds[prds$env == 'KY-2009',]

plot1 <- xyplot(height ~ doy, data = obs.KY.2009, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.KY.2009[prds.KY.2009$Nrate == 0,]$doy,

 prds.KY.2009[prds.KY.2009$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.KY.2009[prds.KY.2009$Nrate == 60,]$doy,

 prds.KY.2009[prds.KY.2009$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.KY.2009[prds.KY.2009$Nrate == 120,]$doy,

 prds.KY.2009[prds.KY.2009$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'KY-2009', cex = 1.2)

 }, ylab = list('height, m', cex=1), ylim = c(0,4.2), 

 xlim = c(50,290), xlab = list('DOY', cex=1), cex=1.2

 )

 

## Kentucky 2010

obs.KY.2010 <- obs[obs$env == 'KY-2010',]

prds.KY.2010 <- prds[prds$env == 'KY-2010',]

plot2 <- xyplot(height ~ doy, data = obs.KY.2010, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.KY.2010[prds.KY.2010$Nrate == 0,]$doy,

 prds.KY.2010[prds.KY.2010$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.KY.2010[prds.KY.2010$Nrate == 60,]$doy,

 prds.KY.2010[prds.KY.2010$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.KY.2010[prds.KY.2010$Nrate == 120,]$doy,

 prds.KY.2010[prds.KY.2010$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'KY-2010', cex = 1.2)

 }, ylab = list('height, m', cex=1), ylim = c(0,4.2), 

 xlim = c(50,290), xlab = list('DOY', cex=1), cex=1.2

 )

## Nebraska 2009

obs.NE.2009 <- obs[obs$env == 'NE-2009',]

prds.NE.2009 <- prds[prds$env == 'NE-2009',]

plot3 <- xyplot(height ~ doy, data = obs.NE.2009, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.NE.2009[prds.NE.2009$Nrate == 0,]$doy,

 prds.NE.2009[prds.NE.2009$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.NE.2009[prds.NE.2009$Nrate == 60,]$doy,

 prds.NE.2009[prds.NE.2009$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.NE.2009[prds.NE.2009$Nrate == 120,]$doy,

 prds.NE.2009[prds.NE.2009$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'NE-2009', cex = 1.2)

 }, ylab = list('height, m', cex=1), ylim = c(0,4.2), 

 xlim = c(50,290), xlab = list('DOY', cex=1), cex=1.2

 )

## Nebraska 2010
147

obs.NE.2010 <- obs[obs$env == 'NE-2010',]

prds.NE.2010 <- prds[prds$env == 'NE-2010',]

plot4 <- xyplot(height ~ doy, data = obs.NE.2010, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.NE.2010[prds.NE.2010$Nrate == 0,]$doy,

 prds.NE.2010[prds.NE.2010$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.NE.2010[prds.NE.2010$Nrate == 60,]$doy,

 prds.NE.2010[prds.NE.2010$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.NE.2010[prds.NE.2010$Nrate == 120,]$doy,

 prds.NE.2010[prds.NE.2010$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'NE-2010', cex = 1.2)

 }, ylab = list('height, m', cex=1), ylim = c(0,4.2), 

 xlim = c(50,290), xlab = list('DOY', cex=1), cex=1.2

 )

## New Jersey 2009

obs.NJ.2009 <- obs[obs$env == 'NJ-2009',]

prds.NJ.2009 <- prds[prds$env == 'NJ-2009',]

plot5 <- xyplot(height ~ doy, data = obs.NJ.2009, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.NJ.2009[prds.NJ.2009$Nrate == 0,]$doy,

 prds.NJ.2009[prds.NJ.2009$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.NJ.2009[prds.NJ.2009$Nrate == 60,]$doy,

 prds.NJ.2009[prds.NJ.2009$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.NJ.2009[prds.NJ.2009$Nrate == 120,]$doy,

 prds.NJ.2009[prds.NJ.2009$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'NJ-2009', cex = 1.2)

 }, ylab = list('height, m', cex=1), ylim = c(0,4.2), 

 xlim = c(50,290), xlab = list('DOY', cex=1), cex=1.2

 )

## New Jersey 2010

obs.NJ.2010 <- obs[obs$env == 'NJ-2010',]

prds.NJ.2010 <- prds[prds$env == 'NJ-2010',]

plot6 <- xyplot(height ~ doy, data = obs.NJ.2010, groups = Nrate,

 col = 'black', fill = 'transparent', 

 panel = function(x,y,...){

 panel.xyplot(x,y, type = 'p', pch=c(17,19,22), ...)

 panel.xyplot(prds.NJ.2010[prds.NJ.2010$Nrate == 0,]$doy,

 prds.NJ.2010[prds.NJ.2010$Nrate == 0,]$preds, type='l', lty = 1,...)

 panel.xyplot(prds.NJ.2010[prds.NJ.2010$Nrate == 60,]$doy,

 prds.NJ.2010[prds.NJ.2010$Nrate == 60,]$preds, type='l', lty = 2,...)

 panel.xyplot(prds.NJ.2010[prds.NJ.2010$Nrate == 120,]$doy,

 prds.NJ.2010[prds.NJ.2010$Nrate == 120,]$preds, type='l', lty = 3,...)

 panel.text(170,4, labels = 'NJ-2010', cex = 1.2)

 }, ylab = list('height, m', cex=1),

 ylim = c(0,4.2), 

 xlim = c(50,290),

 xlab = list('DOY', cex=1), cex = 1.2,

 key=list(x=0.5, y=0.25, cex.title=1.2, 

 pch = c(17, 19, 22),

 lty = c(1,2,3), points = TRUE, lines = TRUE,

 text=list(c("0 N", "60 N", "120 N"))))

png('../figs/ht 2009 2010.png', width=2200, height=2800, res=225)

print(plot1, c(0, 0.666, 0.5, 1), more = TRUE)

print(plot2, c(0.5, 0.666, 1, 1), more = TRUE)

print(plot3, c(0, 0.333, 0.5, 0.666), more = TRUE)
148

print(plot4, c(0.5, 0.333, 1, 0.666), more = TRUE)

print(plot5, c(0, 0, 0.5, 0.333), more = TRUE)

print(plot6, c(0.5, 0, 1, 0.333))

dev.off()

## Plot predictions

png('../figs/augPred-final 2009 2010 ht.nlme.png', width=1600, height=1600, res=145)

plot(augPred(fit6.nlme, level = 0:1),

 lty=c(1,2), col=c("black"),as.table=T,

 key=list(text=list(c("fixed", "random")), lty=c(1,2), lines=T),

 ylab = list('height, m', cex=1.2),

 xlab = list('day of year', cex=1.2))

dev.off()

Biomass yields-Ch.3, Table 3.7

*DOE miscanthus manuscript, 2nd and 3rd year biomass yield;

options ls=120 ps=100 nodate nocenter formdlim="-";

data biomass;

length location$ 10;

length harvest$ 10;

infile 'F:\PhD\Miscanthus fertility study\DOE 2009 2010 growth and yield manuscript\2009 2010 

season biomass yields, NE, KY, NJ, IL.csv'

dlm="," firstobs=4;

input year location$ block subsample Nrate biomass harvest$;

label Nrate="N rate (kg N/ha)";

label biomass="Dry Biomass (Mg/ha)";

*if location="Illinois" then delete;

*if location="Illinois" and year="2009" then Env="IL09";

*if location="Illinois" and year="2010" then Env="IL10";

if location="Kentucky" and year="2009" then Env="KY09";

if location="Kentucky" and year="2010" then Env="KY10";

if location="Nebraska" and year="2009" then Env="NE09";

if location="Nebraska" and year="2010" then Env="NE10";

if location="New Jersey" and year="2009" then Env="NJ09";

if location="New Jersey" and year="2010" then Env="NJ10";

*dropping IL, b/c in poor winter survival in 2008-2009 required ~80% replanting. As of 2010 the 

IL

stand is a mixture of 2 and 3 year old plants. This is not on par with the other 3 sites, all of

which are 3 year old stands as of 2010.;

cards;

run; proc print; run;

data Il; set biomass; 

if location ne "Illinois" then delete;

run;

proc sort data=IL; by year Nrate; run;

proc means data=IL mean noprint; var biomass; by year Nrate;

output out=ILmeans mean=biomass; run; proc print data=ILmeans; run;

*running a model with location and year combined into an environment effect.

model:

random: Environment(E) = KY09 KY10 NE09 NE10 NJ09 NJ10 i=1,2,3

random: block(B) = 1,2,3,4 j=1,2,3,4

fixed: N rate-(N) = 0,60,120 k=1,2,3

random: subsample-(S) = 1 to 4 depending on site l=1 to 4

*main questions:

Is there an N response, what type (linear/quadratic), what locations?

Is there a significant increase or decrease in yield from 2009 to 2010 at each site?;

proc mixed data=biomass covtest method=REML;

class Env Nrate block;

model biomass=Nrate/ddfm=kr outpred=temp;

random Env block(Env) Env*Nrate Nrate*block(Env); 

*should block be nested in Env? We know it is nested in location.;

estimate "KY09 vs KY10"|Env 1 -1 0 0 0 0;

estimate "NE09 vs NE10"|Env 0 0 1 -1 0 0;

estimate "NJ09 vs NJ10"|Env 0 0 0 0 1 -1;
149

*BLUP Env lsmeans;

estimate "KY09" intercept 1|Env 1 0 0 0 0 0;

estimate "KY10" intercept 1|Env 0 1 0 0 0 0;

estimate "NE09" intercept 1|Env 0 0 1 0 0 0;

estimate "NE10" intercept 1|Env 0 0 0 1 0 0;

estimate "NJ09" intercept 1|Env 0 0 0 0 1 0;

estimate "NJ10" intercept 1|Env 0 0 0 0 0 1;

*BLUP Env*Nrate lsmeans;

estimate "KY09 Nrate 0" intercept 1 Nrate 1|

Env 1 Env*Nrate 1;

estimate "KY09 Nrate 60" intercept 1 Nrate 0 1|

Env 1 Env*Nrate 0 1;

estimate "KY09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 1 Env*Nrate 0 0 1;

estimate "KY10 Nrate 0" intercept 1 Nrate 1|

Env 0 1 Env*Nrate 0 0 0 1;

estimate "KY10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 1 Env*Nrate 0 0 0 0 1;

estimate "KY10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 1 Env*Nrate 0 0 0 0 0 1;

estimate "NE09 Nrate 0" intercept 1 Nrate 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 1;

estimate "NE09 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 1;

estimate "NE09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "NE10 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "NE10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "NE10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ09 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ09 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ10 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "NJ10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

*Nlinear effect within environment;

estimate "KY-09 linear" Nrate 1 0 -1|Env*Nrate 1 0 -1;

estimate "KY-10 linear" Nrate 1 0 -1|Env*Nrate 0 0 0 1 0 -1;

estimate "NE-09 linear" Nrate 1 0 -1|

Env*Nrate 0 0 0 0 0 0 1 0 -1;

estimate "NE-10 linear" Nrate 1 0 -1|

Env*Nrate 0 0 0 0 0 0 0 0 0 1 0 -1;

estimate "NJ-09 linear" Nrate 1 0 -1|

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -1;

estimate "NJ-10 linear" Nrate 1 0 -1|

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -1;

*Nquad effect within environment;

estimate "KY-09 quad" Nrate 1 -2 1|Env*Nrate 1 -2 1;

estimate "KY-10 quad" Nrate 1 -2 1|Env*Nrate 0 0 0 1 -2 1;

estimate "NE-09 quad" Nrate 1 -2 1|

Env*Nrate 0 0 0 0 0 0 1 -2 1;

estimate "NE-10 quad" Nrate 1 -2 1|

Env*Nrate 0 0 0 0 0 0 0 0 0 1 -2 1;

estimate "NJ-09 quad" Nrate 1 -2 1|

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1 -2 1;

estimate "NJ-10 quad" Nrate 1 -2 1|

Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -2 1;

run;
150

goptions reset=all;

symbol v=plus color=blue h=1.5;

proc gplot data=temp;

plot resid*pred/ vref=0 vaxis=-10 to 10 by 2;

run;

symbol v=circle color=blue h=2;

proc univariate data=temp normal plot; var resid;

probplot resid/ normal(mu=est sigma=est color=black w=3);

inset n mean median min max skewness kurtosis var probn;

run;

Tiller density-Ch.3, Table 3.7.

*2009 end of season stems;

options ls=100 ps=100 nodate nocenter formdlim="-";

data grwth09;

length loc$ 10;

length month$ 10;

infile 'F:\PhD\Miscanthus fertility study\DOE manuscript, 2nd year growth\2009 growth data\2009 

growth data, NE, KY, NJ.csv'

dlm="," firstobs=4;

input year$ loc$ block month$ date$ Nrate Nlb ht stems leaves TT;

*keeping final sampling dates at each location for stem #;

*KY=Sept 4, 2009, NE=Sept 15, 2009, NJ=Oct 23, 2009;

if loc="Nebraska" and month="May" then delete;

if loc="Nebraska" and month="June" then delete;

if loc="Nebraska" and month="July" then delete;

if loc="Nebraska" and month="August" then delete;

if loc="Kentucky" and month="April" then delete;

if loc="Kentucky" and month="May" then delete;

if loc="Kentucky" and month="June" then delete;

if loc="Kentucky" and month="July" then delete;

if loc="New Jersey" and month="May" then delete;

if loc="New Jersey" and month="June" then delete;

if loc="New Jersey" and month="July" then delete;

if loc="New Jersey" and month="August" then delete;

if loc="New Jersey" and month="September" then delete;

drop date Nlb ht leaves TT;

sqstems=sqrt(stems);

run; proc print data=grwth09; run;

data shoot09; set grwth09;

drop month; run; proc print data=shoot09; run;

*2010 stem number for KY, NE, IL, NJ;

Data stemnum;

length loc$ 10;

Infile 'F:\PhD\Miscanthus fertility study\DOE 2009 2010 growth and yield manuscript\2010 stem 

number.csv'

firstobs=4 dlm=',';

input location$ year$ plotID$ block grass Nrate harvest$ plant_sub stems biomass;

drop grass plotID harvest plant_sub;

if location="IL" then delete;

if location="KY" then loc= "Kentucky";

if location="NE" then loc= "Nebraska";

if location="NJ" then loc= "New Jersey";

sqrtstems=sqrt(stems);

run; proc print data=stemnum; run;

/*

data ILstems; set stemnum;

if location ne "IL" then delete;

run; proc print data=ILstems; run;

proc sort data=ILstems; by Nrate; run;

proc means data=ILstems mean; by Nrate; var stems; run;

*/
151

*means of stem number;

proc sort data=stemnum; by loc year block Nrate; run;

proc means data=stemnum noprint mean; var stems sqrtstems;

by loc year block Nrate; 

output out=shoots mean=stems sqstems; run;

proc print data=shoots; run; 

data shoot10; set shoots; 

drop _TYPE_ _FREQ_;

run; proc print data=shoot10; run;

*combining 2009 2010 shoot number;

proc sort data=shoot10; by loc year block Nrate; run;

proc sort data=shoot09; by loc year block Nrate; run;

data shoot; merge shoot10 shoot09;

by loc year block Nrate; run; proc print data=shoot; run;

data stem; set shoot; 

if loc="Kentucky" and year="2009" then Env="KY09";

if loc="Kentucky" and year="2010" then Env="KY10";

if loc="Nebraska" and year="2009" then Env="NE09";

if loc="Nebraska" and year="2010" then Env="NE10";

if loc="New Jersey" and year="2009" then Env="NJ09";

if loc="New Jersey" and year="2010" then Env="NJ10";

run; proc print data=stem; run;

ods html;

ods graphics on;

proc glimmix data=stem plots=(residualpanel pearsonpanel studentpanel);

class Env Nrate block;

model stems=Nrate/dist=Poisson ddfm=kr;

random Env block(Env) Env*Nrate Nrate*block(Env); 

covtest glm/wald;

estimate "KY09 vs KY10"|Env 1 -1 0 0 0 0;

estimate "NE09 vs NE10"|Env 0 0 1 -1 0 0;

estimate "NJ09 vs NJ10"|Env 0 0 0 0 1 -1;

*BLUP Env lsmeans;

estimate "KY09" intercept 1|Env 1 0 0 0 0 0/ilink;

estimate "KY10" intercept 1|Env 0 1 0 0 0 0/ilink;

estimate "NE09" intercept 1|Env 0 0 1 0 0 0/ilink;

estimate "NE10" intercept 1|Env 0 0 0 1 0 0/ilink;

estimate "NJ09" intercept 1|Env 0 0 0 0 1 0/ilink;

estimate "NJ10" intercept 1|Env 0 0 0 0 0 1/ilink;

*BLUP Env*Nrate lsmeans;

estimate "KY09 Nrate 0" intercept 1 Nrate 1|

Env 1 Env*Nrate 1/ilink cl;

estimate "KY09 Nrate 60" intercept 1 Nrate 0 1|

Env 1 Env*Nrate 0 1/ilink cl;

estimate "KY09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 1 Env*Nrate 0 0 1/ilink cl;

estimate "KY10 Nrate 0" intercept 1 Nrate 1|

Env 0 1 Env*Nrate 0 0 0 1/ilink cl;

estimate "KY10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 1 Env*Nrate 0 0 0 0 1/ilink cl;

estimate "KY10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 1 Env*Nrate 0 0 0 0 0 1/ilink cl;

estimate "NE09 Nrate 0" intercept 1 Nrate 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 1/ilink;

estimate "NE09 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 1/ilink;

estimate "NE09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 1/ilink;

estimate "NE10 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NE10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NE10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1/ilink;
152

estimate "NJ09 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NJ09 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NJ09 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NJ10 Nrate 0" intercept 1 Nrate 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NJ10 Nrate 60" intercept 1 Nrate 0 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

estimate "NJ10 Nrate 120" intercept 1 Nrate 0 0 1|

Env 0 0 0 0 0 1 Env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/ilink;

run;

ods graphics off;

ods html close;

Correlations among variables and scatter plot-Ch.3, Fig. 3.2

*2009 end of season ht, stems;

options ls=100 ps=100 nodate nocenter formdlim="-";

data grwth09;

length loc$ 10;

length month$ 10;

infile 'F:\PhD\Miscanthus fertility study\DOE manuscript, 2nd year growth\2009 growth data\2009 

growth data, NE, KY, NJ.csv'

dlm="," firstobs=4;

input year$ loc$ block month$ date$ Nrate Nlb ht stems leaves TT;

*keeping final sampling dates at each location for stem #;

*KY=Sept 4, 2009, NE=Sept 15, 2009, NJ=Oct 23, 2009;

if loc="Nebraska" and month="May" then delete;

if loc="Nebraska" and month="June" then delete;

if loc="Nebraska" and month="July" then delete;

if loc="Nebraska" and month="August" then delete;

if loc="Kentucky" and month="April" then delete;

if loc="Kentucky" and month="May" then delete;

if loc="Kentucky" and month="June" then delete;

if loc="Kentucky" and month="July" then delete;

if loc="New Jersey" and month="May" then delete;

if loc="New Jersey" and month="June" then delete;

if loc="New Jersey" and month="July" then delete;

if loc="New Jersey" and month="August" then delete;

if loc="New Jersey" and month="September" then delete;

run; proc print data=grwth09; run;

proc sort data=grwth09; by loc year block Nrate;

proc means data=grwth09 noprint mean; var ht stems;

by loc year block Nrate; 

output out=height09 mean=ht_mean shoot_mean; run;

proc print data=height09; run; 

data htstem09; set height09; 

drop _TYPE_ _FREQ_;

run; proc print data=htstem09; run;

*2010 end of season ht;

data grwth10;

length loc$ 10;

length month$ 10;

infile 'F:\PhD\Miscanthus fertility study\DOE 2009 2010 growth and yield manuscript\2010 growth 

data, KY, NE, NJ, IL.csv'

dlm="," firstobs=4;

input year$ loc$ block month$ date$ Nrate Nlb ht TT;

*keeping final sampling dates at each location for stem #;

*KY=Oct 12, 2010, NE=Nov 22, 2010, NJ=Nov 2, 2010;

if loc="Illinois" then delete;

if loc="Kentucky" and month="April" then delete;

if loc="Kentucky" and month="May" then delete;
153

if loc="Kentucky" and month="June" then delete;

if loc="Kentucky" and month="July" then delete;

if loc="Kentucky" and month="August" then delete;

if loc="Kentucky" and month="September" then delete;

if loc="Nebraska" and month="April" then delete;

if loc="Nebraska" and month="May" then delete;

if loc="Nebraska" and month="June" then delete;

if loc="Nebraska" and month="July" then delete;

if loc="Nebraska" and month="August" then delete;

if loc="Nebraska" and month="September" then delete;

if loc="Nebraska" and month="October" then delete;

if loc="New Jersey" and month="May" then delete;

if loc="New Jersey" and month="June" then delete;

if loc="New Jersey" and month="July" then delete;

if loc="New Jersey" and month="August" then delete;

if loc="New Jersey" and month="September" then delete;

if loc="New Jersey" and month="October" then delete;

run; proc print data=grwth10; run;

proc sort data=grwth10; by loc year block Nrate;

proc means data=grwth10 noprint mean; var ht;

by loc year block Nrate; 

output out=height10 mean=ht_mean; run;

proc print data=height10; run; 

data ht10; set height10; 

drop _TYPE_ _FREQ_;

run; proc print data=ht10; run;

*2010 end of season KY, NE, IL, NJ;

Data stem10;

length loc$ 10;

Infile 'F:\PhD\Miscanthus fertility study\DOE 2010 yield components\2010 stem number.csv'

firstobs=4 dlm=',';

input location$ year$ plotID$ block grass Nrate harvest$ plant_sub shoots;

drop grass;

if location="IL" then delete;

if location="KY" then loc= "Kentucky";

if location="NE" then loc= "Nebraska";

if location="NJ" then loc= "New Jersey";

run; proc print data=stem10; run;

*means of stem number;

proc sort data=stem10; by loc year block Nrate; run;

proc means data=stem10 noprint mean; var shoots;

by loc year block Nrate; 

output out=shoots mean=shoot_mean; run;

proc print data=shoots; run; 

data shoot10; set shoots; 

drop _TYPE_ _FREQ_;

run; proc print data=shoot10; run;

*2010 stem number;

Data stemnum;

Infile 'F:\PhD\Miscanthus fertility study\DOE 2010 yield components\2010 stem number.csv'

firstobs=4 dlm=',';

input loc$ year plotID$ rep grass Nrate harvest$ plant_sub shoots biomass;

drop grass;

run; proc print data=stemnum; run;

*combining 2010 ht shoot number;

data comb10; merge shoot10 ht10;

by loc year block Nrate; run; proc print data=comb10; run;

* 2nd and 3rd year biomass yield;
154

options ls=120 ps=100 nodate nocenter formdlim="-";

data biomass;

length loc$ 10;

length harvest$ 10;

infile 'F:\PhD\Miscanthus fertility study\DOE 2009 2010 growth and yield manuscript\2009 2010 

season biomass yields, NE, KY, NJ, IL.csv'

dlm="," firstobs=4;

input year$ loc$ block subsample Nrate biomass harvest$;

label Nrate="N rate (kg N/ha)";

label biomass="Dry Biomass (Mg/ha)";

if loc="Illinois" then delete; 

if loc="Kentucky" and year="2009" then Env="KY09";

if loc="Kentucky" and year="2010" then Env="KY10";

if loc="Nebraska" and year="2009" then Env="NE09";

if loc="Nebraska" and year="2010" then Env="NE10";

if loc="New Jersey" and year="2009" then Env="NJ09";

if loc="New Jersey" and year="2010" then Env="NJ10";

cards;

run; proc print; run;

data biomass09; set biomass; 

if year=2010 then delete;

run; proc print data=biomass09; run;

*means of biomasss 2009;

proc sort data=biomass09; by loc year block Nrate; run;

proc means data=biomass09 noprint mean; var biomass;

by loc year block Nrate; 

output out=bio09 mean=bio_mean; run;

proc print data=bio09; run; 

data biom09; set bio09; 

drop _TYPE_ _FREQ_;

run; proc print data=biom09; run;

data biomass10; set biomass; 

if year=2009 then delete;

run; proc print data=biomass10; run;

*means of biomass 2010;

proc sort data=biomass10; by loc year block Nrate; run;

proc means data=biomass10 noprint mean; var biomass;

by loc year block Nrate; 

output out=bio10 mean=bio_mean; run;

proc print data=bio10; run; 

data biom10; set bio10; 

drop _TYPE_ _FREQ_;

run; proc print data=biom10; run;

*combining 2009 ht, shoot, biomass;

data all2009;

label loc=location;

label ht_mean=plant ht (m);

label shoot_mean=tiller density (tillers/sq. m);

label bio_mean=dry biomass (Mg/ha);

merge htstem09 biom09;

by loc year block Nrate; run; 

proc print data=all2009; run;

data all2009; set all2009;

if loc="Kentucky" and year="2009" then Env="KY-2009";

if loc="Nebraska" and year="2009" then Env="NE-2009";

if loc="New Jersey" and year="2009" then Env="NJ-2009";

label env=environment;

run; proc print data=all2009; run;

proc sgscatter data=all2009;

title "2009 season";

matrix ht_mean shoot_mean bio_mean/group=env;

run;

proc corr data=all2009; 

var ht_mean shoot_mean bio_mean; run;
155

*combining 2010 ht, shoot, biomass;

data all2010; 

label loc=location;

label ht_mean=plant ht (m);

label shoot_mean=tiller density (tillers/sq. m);

label bio_mean=dry biomass (Mg/ha);

merge comb10 biom10;

by loc year block Nrate; 

run; proc print data=all2010; run;

data all2010; set all2010;

if loc="Kentucky" and year="2010" then Env="KY-2010";

if loc="Nebraska" and year="2010" then Env="NE-2010";

if loc="New Jersey" and year="2010" then Env="NJ-2010";

label env=environment;

run; proc print data=all2010; run;

proc sgscatter data=all2010;

title "2010 season";

matrix ht_mean shoot_mean bio_mean/group=env;

run;

proc corr data=all2010; 

var ht_mean shoot_mean bio_mean; run;
156

APPENDIX C

Selected SAS and R code for Ch. 4

Plant height and expanded leaf number-Ch.4, Fig. 4.1, Fig. 4.3

library(nlme)

library(Hmisc)

library(lattice)

library (graphics)

#2009 & 2010 sorghum ht, leafno data

htleaf <- read.csv(file = "./2009 & 2010 analysis for manuscript 1/sorghum ht-leafno 2009 & 

2010.csv", skip = 4, na.strings=".")

htleaf$height <- htleaf$ht..cm. / 100

head(htleaf)

htleaf <- htleaf[,-2] #getting rid of Date

htleaf <- htleaf[,-4] #getting rid of sample

htleaf <- htleaf[,-7] #getting rid of nitrogen lb/acre

htleaf <- htleaf[,-7] #getting rid of plot

htleaf <- htleaf[,-8] #getting rid of height expressed in cm

htleaf <- htleaf[,-8] #getting rid of comments

htleaf <- na.omit(htleaf) #getting rid of observations with missing ht and leafno data

names(htleaf) <- c('Environment', 'doy', 'block', 'type', 'variety', 'Nrate', 'leafno', 'GDD10', 

'GDD11', 'height')

head(htleaf)

UrbE09 <-htleaf[htleaf$Environment == "Urbana-2009" & htleaf$type == "Energy",]

unique(UrbE09$doy) #194 210 231 261 280

UrbE10 <-htleaf[htleaf$Environment == "Urbana-2010" & htleaf$type == "Energy",]

unique(UrbE10$doy) #176 202 243 270

DSE10 <-htleaf[htleaf$Environment == "Dixon Springs-2010" & htleaf$type == "Energy",]

unique(DSE10$doy) #181 200 217 277

PerE10 <-htleaf[htleaf$Environment == "Perry-2010" & htleaf$type == "Energy",]

unique(PerE10$doy) #182 204 229 276

#plotting for leaf number

energy <-htleaf[htleaf$type == "Energy",]

forage <-htleaf[htleaf$type == "Forage" & htleaf$variety == "Graze. n. Bale",] #dropping 

photperiod insensitive variety, Graze All

forage[forage$doy > 242,2]

forage[forage$doy > 242,2] <- NA #getting rid of leafno beyond the first harvest

forage <- na.omit(forage)

head(forage)

leafno <- rbind(energy, forage)

head(leafno)

unique(leafno$Environment) #Dixon Springs-2010 Perry-2010 Urbana-2009 Urbana-2010

xyplot(leafno ~ GDD11, data = energy, type=c("p", "r"), groups = Environment, auto.key=TRUE)

xyplot(leafno ~ GDD11, data = forage, type=c("p", "r"), groups = Environment, auto.key=TRUE)

png("./2009 & 2010 analysis for manuscript 1/leafno.png",width=600, height=600, res=80)

xyplot(leafno ~ GDD11, data = energy, groups = Environment,

 xlab=list("GDD",cex=1.2),

 ylab=list("expanded leaf number",cex=1.3),

 pch=c(17, 19, 22, 8), col=c("darkgreen","darkred","midnightblue","black"), 

 fill = 'transparent', lty = c(1,2,3,4), lwd=2,

 type=c("p","r"), cex = 1.2, 

 key=list(x=0.3, y=1, cex.title=1.3, 

 pch = c(17, 19, 22, 8), cex=1.3,

 lty = c(1,2,3,4), points = TRUE, lines = TRUE, lwd=2,

 col=c("darkgreen","darkred","midnightblue","black"),

 text=list(c("Dixon Springs 2010", "Perry 2010", "Urbana 2009", "Urbana 

2010"))))

 dev.off()

mean(leafno[leafno$doy == "277" & leafno$Environment == "Dixon Springs-2010","leafno"]) #28.25

mean(leafno[leafno$doy == "276" & leafno$Environment == "Perry-2010","leafno"]) #28.42

 
157

## dataframe for Urbana Energy 2009

UrbE09.1 <- data.frame(env = UrbE09[,1],

 doy = UrbE09[,2],

variety = UrbE09[,5],

Nrate = UrbE09[,6],

leafno = UrbE09[,7],

GDD10 = UrbE09[,8],

GDD11 = UrbE09[,9],

height = UrbE09[,10])

head(UrbE09.1)

## developing a model Urbana Energy 2009 height

expu <- with(UrbE09, Environment:factor(block):factor(variety):factor(Nrate))

UrbE09G <- groupedData(height ~ doy | expu, data = UrbE09.1)

head(UrbE09G)

UrbE09ht.nls <- nlsList(height ~ SSlogis(doy, Asym, xmid, scal), data =UrbE09G)

UrbE09ht.nlme <- nlme(UrbE09ht.nls)

anova(UrbE09ht.nlme)

plot(augPred(UrbE09ht.nlme, level = 0:1))

coef(UrbE09ht.nlme)

## dataframe for Urbana Energy 2010

UrbE10.1 <- data.frame(env = UrbE10[,1],

 doy = UrbE10[,2],

variety = UrbE10[,5],

Nrate = UrbE10[,6],

 leafno = UrbE10[,7],

 GDD10 = UrbE10[,8],

GDD11 = UrbE10[,9],

height = UrbE10[,10])

head(UrbE10.1)

## developing a model Urbana Energy 2010 height

expu <- with(UrbE10, Environment:factor(block):factor(variety):factor(Nrate))

UrbE10G <- groupedData(height ~ doy | expu, data = UrbE10.1)

head(UrbE10G)

UrbE10ht.nls <- nlsList(height ~ SSlogis(doy, Asym, xmid, scal), 

 data =UrbE10G,

 start = c(Asym = 3.5, xmid = 220, scal = 25))

UrbE10ht.nlme <- nlme(UrbE10ht.nls)

anova(UrbE10ht.nlme)

plot(augPred(UrbE10ht.nlme, level = 0:1))

coef(UrbE10ht.nlme)

## dataframe for Dixon Springs Energy 2010

DSE10.1 <- data.frame(env = DSE10[,1],

 doy = DSE10[,2],

 variety = DSE10[,5],

 Nrate = DSE10[,6],

leafno = DSE10[,7],

GDD10 = DSE10[,8],

GDD11 = DSE10[,9],

height = DSE10[,10])

head(DSE10.1)

## developing a model Dixon Springs Energy 2010 height 

expu <- with(DSE10, Environment:factor(block):factor(variety):factor(Nrate))

DSE10G <- groupedData(height ~ doy | expu, data = DSE10.1)

head(DSE10G)

xyplot(height ~ doy, data = DSE10G, groups = Nrate, auto.key=TRUE)

DSE10ht.nls <- nlsList(height ~ SSlogis(doy, Asym, xmid, scal), 

 data =DSE10G,

 start = c(Asym = 3.5, xmid = 230, scal = 20))

DSE10ht.nlme <- nlme(DSE10ht.nls)

anova(DSE10ht.nlme)

plot(augPred(DSE10ht.nlme, level = 0:1))

coef(DSE10ht.nlme)

## dataframe for Perry Energy 2010
158

PerE10.1 <- data.frame(env = PerE10[,1],

 doy = PerE10[,2],

variety = PerE10[,5],

Nrate = PerE10[,6],

leafno = PerE10[,7],

GDD10 = PerE10[,8],

GDD11 = PerE10[,9],

height = PerE10[,10])

head(PerE10.1)

## developing a model Perry Energy 2010 height

expu <- with(PerE10, Environment:factor(block):factor(variety):factor(Nrate))

PerE10G <- groupedData(height ~ doy | expu, data = PerE10.1)

head(PerE10G)

xyplot(height ~ doy, data = PerE10G, groups = Nrate, auto.key=TRUE)

PerE10ht.nls <- nlsList(height ~ SSlogis(doy, Asym, xmid, scal), 

 data =PerE10G,

 start = c(Asym = 3.9, xmid = 230, scal = 30))

PerE10ht.nlme <- nlme(PerE10ht.nls)

anova(PerE10ht.nlme)

plot(augPred(PerE10ht.nlme, level = 0:1))

coef(PerE10ht.nlme)

*Bringing in logistic fitted paramteres for energy sorghum height

options ls=120 ps=150 nodate nocenter formdlim="-";

data locyr;

length loc$ 14;

length hybrid$ 11;

infile 'C:\Documents and Settings\maughan2\My Documents\My Dropbox\sorghum fertility trial\2009 & 

2010 analysis for manuscript 1\Energy sorghum logistic fits, LOC-YR.csv'

dlm="," firstobs=3;

input loc$ yr block hybrid$ Nrate Asym xmid scal;

if loc="Urbana" and yr=2010 then env="Urb2010";

if loc="Dixon Springs" and yr=2010 then env="DS2010";

if loc="Perry" and yr=2010 then env="Per2010";

cards;

run; proc print; run;

*Urbana 2009 analyzed alone;

data Urbana09; set locyr;

if loc ne "Urbana" then delete;

if yr ne 2009 then delete;

run; proc print data=Urbana09; run;

*Asym;

ods html;

ods graphics on;

proc glimmix data=Urbana09 plots=RESIDUALPANEL;

class block hybrid Nrate;

model Asym=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/pdiff;

contrast "0 vs all N" Nrate 1 -.33333 -.33333 -.33333;

contrast "0 vs 50" Nrate 1 -1;

contrast "0 vs 100" Nrate 1 0 -1;

contrast "0 vs 150" Nrate 1 0 0 -1;

contrast "50 vs 100" Nrate 0 1 -1 0;

contrast "50 vs 150" Nrate 0 1 0 -1;

contrast "100 vs 150" Nrate 0 0 1 -1;

run;

ods graphics off;

ods html close;

*xmid;

ods html;

ods graphics on;
159

proc glimmix data=Urbana09 plots=RESIDUALPANEL;

class block hybrid Nrate;

model xmid=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/pdiff;

contrast "0 vs all N" Nrate 1 -.33333 -.33333 -.33333;

contrast "0 vs 50" Nrate 1 -1;

contrast "0 vs 100" Nrate 1 0 -1;

contrast "0 vs 150" Nrate 1 0 0 -1;

contrast "50 vs 100" Nrate 0 1 -1 0;

contrast "50 vs 150" Nrate 0 1 0 -1;

contrast "100 vs 150" Nrate 0 0 1 -1;

run;

ods graphics off;

ods html close;

*scal;

ods html;

ods graphics on;

proc glimmix data=Urbana09 plots=RESIDUALPANEL;

class block hybrid Nrate;

model scal=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;

random block block*hybrid;

covtest glm/wald;

lsmeans Nrate/pdiff;

contrast "0 vs all N" Nrate 1 -.33333 -.33333 -.33333;

contrast "0 vs 50" Nrate 1 -1;

contrast "0 vs 100" Nrate 1 0 -1;

contrast "0 vs 150" Nrate 1 0 0 -1;

contrast "50 vs 100" Nrate 0 1 -1 0;

contrast "50 vs 150" Nrate 0 1 0 -1;

contrast "100 vs 150" Nrate 0 0 1 -1;

run;

ods graphics off;

ods html close;

*2010 data considering environments random;

data env2010; set locyr;

if yr=2009 then delete;

run; proc print; 

*Asym;

ods html;

ods graphics on;

proc glimmix data=env2010 plots=RESIDUALPANEL;

class env block hybrid Nrate;

model Asym=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1|env 1 env*Nrate 1;

estimate "DS 56" intercept 1 Nrate 0 1|env 1 env*Nrate 0 1;

estimate "DS 112" intercept 1 Nrate 0 0 1|env 1 env*Nrate 0 0 1;

estimate "DS 168" intercept 1 Nrate 0 0 0 1|env 1 env*Nrate 0 0 0 1;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1|env 1 env*Nrate 0 0 0 0 1;

estimate "PE 0" intercept 1 Nrate 1|env 0 1 env*Nrate 0 0 0 0 0 1;

estimate "PE 56" intercept 1 Nrate 0 1|env 0 1 env*Nrate 0 0 0 0 0 0 1;

estimate "PE 112" intercept 1 Nrate 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 1;

estimate "PE 168" intercept 1 Nrate 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "UR 0" intercept 1 Nrate 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 56" intercept 1 Nrate 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 112" intercept 1 Nrate 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;
160

estimate "UR 168" intercept 1 Nrate 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

contrast "0N vs. Napp" Nrate 1 -.25 -.25 -.25 -.25;

contrast "0 vs 56" Nrate 1 -1;

contrast "0 vs 112" Nrate 1 0 -1;

contrast "0 vs 168" Nrate 1 0 0 -1;

contrast "0 vs 224" Nrate 1 0 0 0 -1;

contrast "56 vs 112" Nrate 0 1 -1 0 0;

contrast "56 vs 168" Nrate 0 1 0 -1 0;

contrast "56 vs 224" Nrate 0 1 0 0 -1;

contrast "112 vs 168" Nrate 0 0 1 -1 0;

contrast "112 vs 224" Nrate 0 0 1 0 -1;

contrast "168 vs 224" Nrate 0 0 0 1 -1;

run;

ods graphics off;

ods html close;

*xmid;

ods html;

ods graphics on;

proc glimmix data=env2010 plots=RESIDUALPANEL;

class env block hybrid Nrate;

model xmid=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1|env 1 env*Nrate 1;

estimate "DS 56" intercept 1 Nrate 0 1|env 1 env*Nrate 0 1;

estimate "DS 112" intercept 1 Nrate 0 0 1|env 1 env*Nrate 0 0 1;

estimate "DS 168" intercept 1 Nrate 0 0 0 1|env 1 env*Nrate 0 0 0 1;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1|env 1 env*Nrate 0 0 0 0 1;

estimate "PE 0" intercept 1 Nrate 1|env 0 1 env*Nrate 0 0 0 0 0 1;

estimate "PE 56" intercept 1 Nrate 0 1|env 0 1 env*Nrate 0 0 0 0 0 0 1;

estimate "PE 112" intercept 1 Nrate 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 1;

estimate "PE 168" intercept 1 Nrate 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "UR 0" intercept 1 Nrate 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 56" intercept 1 Nrate 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 112" intercept 1 Nrate 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 168" intercept 1 Nrate 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

contrast "0N vs. Napp" Nrate 1 -.25 -.25 -.25 -.25;

contrast "0 vs 56" Nrate 1 -1;

contrast "0 vs 112" Nrate 1 0 -1;

contrast "0 vs 168" Nrate 1 0 0 -1;

contrast "0 vs 224" Nrate 1 0 0 0 -1;

contrast "56 vs 112" Nrate 0 1 -1 0 0;

contrast "56 vs 168" Nrate 0 1 0 -1 0;

contrast "56 vs 224" Nrate 0 1 0 0 -1;

contrast "112 vs 168" Nrate 0 0 1 -1 0;

contrast "112 vs 224" Nrate 0 0 1 0 -1;

contrast "168 vs 224" Nrate 0 0 0 1 -1;

run;

ods graphics off;

ods html close;

*scal;

ods html;

ods graphics on;

proc glimmix data=env2010 plots=RESIDUALPANEL;

class env block hybrid Nrate;

model scal=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr;
161

random env block(env) env*hybrid hybrid*block(env) env*Nrate env*hybrid*Nrate;

covtest glm/wald;

estimate "DS 0" intercept 1 Nrate 1|env 1 env*Nrate 1;

estimate "DS 56" intercept 1 Nrate 0 1|env 1 env*Nrate 0 1;

estimate "DS 112" intercept 1 Nrate 0 0 1|env 1 env*Nrate 0 0 1;

estimate "DS 168" intercept 1 Nrate 0 0 0 1|env 1 env*Nrate 0 0 0 1;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1|env 1 env*Nrate 0 0 0 0 1;

estimate "PE 0" intercept 1 Nrate 1|env 0 1 env*Nrate 0 0 0 0 0 1;

estimate "PE 56" intercept 1 Nrate 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 1;

estimate "PE 112" intercept 1 Nrate 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 1;

estimate "PE 168" intercept 1 Nrate 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1|env 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "UR 0" intercept 1 Nrate 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 56" intercept 1 Nrate 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 112" intercept 1 Nrate 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 168" intercept 1 Nrate 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

contrast "0N vs. Napp" Nrate 1 -.25 -.25 -.25 -.25;

contrast "0 vs 56" Nrate 1 -1;

contrast "0 vs 112" Nrate 1 0 -1;

contrast "0 vs 168" Nrate 1 0 0 -1;

contrast "0 vs 224" Nrate 1 0 0 0 -1;

contrast "56 vs 112" Nrate 0 1 -1 0 0;

contrast "56 vs 168" Nrate 0 1 0 -1 0;

contrast "56 vs 224" Nrate 0 1 0 0 -1;

contrast "112 vs 168" Nrate 0 0 1 -1 0;

contrast "112 vs 224" Nrate 0 0 1 0 -1;

contrast "168 vs 224" Nrate 0 0 0 1 -1;

run;

ods graphics off;

ods html close;

Leaf area index-Ch.4, Fig. 4.2

*Urbana 2009 and Urbana 2010 LAI;

options ls=120 ps=150 nodate nocenter formdlim="-";

data lai;

length yr$ 11;

length hybrid$ 14;

infile 'C:\Documents and Settings\maughan2\My Documents\My Dropbox\sorghum fertility trial\2009 & 

2010 analysis for manuscript 1\sorghum LAI 2009 & 2010.csv'

dlm="," firstobs=7;

input yr$ day$ doy block type$ hybrid$ Nrate sub plot abovePAR Par1 tao1 lai zenith hours$ month$ 

comment$ GDD11 intpar;

penetr=100-intpar;

drop day sub plot Par1 tao1 zenith hours month comment;

if type="Forage" then delete;

if abovePAR le 1400 then delete;

cards;

run; proc print; run;

*averaging subsamples;

proc sort data=lai; by yr doy block hybrid Nrate GDD11;

proc means data=lai mean noprint; by yr doy block hybrid Nrate GDD11; var lai intpar ;

output out=laimean mean=lai intpar; run; proc print data=laimean; run;

data laimean; set laimean; 

drop _TYPE_; drop _FREQ_; run; proc print data=laimean; run;

data lai09; set laimean;

if yr ne "Urbana 2009" then delete;
162

run; proc print; 

proc corr data=lai09;

var lai intpar; run;

data lai10; set laimean;

if yr ne "Urbana 2010" then delete;

run; proc print; 

proc corr data=lai10;

var lai intpar; run;

*2009 LAI;

ods html;

ods graphics on;

proc glimmix data=lai09 plots=all;

class block hybrid Nrate doy;

model lai = hybrid Nrate hybrid*Nrate doy hybrid*doy Nrate*doy hybrid*Nrate*doy/dist=normal 

ddfm=KR;

random block block*hybrid block*hybrid*Nrate;

random doy/type=arh(1) subject=block*hybrid*Nrate residual;

covtest glm/wald;

lsmeans Nrate*doy/plot=meanplot(sliceby=Nrate ilink join);

lsmeans Nrate*doy/pdiff;

contrast "DOY July 13 vs Aug. 29" DOY 1 0 -1 0;

contrast "DOY Aug. 29 vs Sept 18" DOY 0 0 1 -1;

contrast "Nrate 0 vs. rest" Nrate 1 -.33333 -.33333 -.33333;

run;

ods graphics off;

ods html close;

proc sort data=lai09; by Nrate doy;

proc means data=lai09 mean noprint; by Nrate doy; var lai intpar;

output out=lai09mean mean=lai intpar; run; proc print data=lai09mean; run;

*2010 LAI;

ods html;

ods graphics on;

proc glimmix data=lai10 plots=all;

class block hybrid Nrate doy;

model lai = hybrid Nrate hybrid*Nrate doy hybrid*doy Nrate*doy hybrid*Nrate*doy/dist=normal 

ddfm=KR;

random block block*hybrid block*hybrid*Nrate;

random doy/type=arh(1) subject=block*hybrid*Nrate residual;

covtest glm/wald;

lsmeans Nrate*doy/plot=meanplot(sliceby=Nrate ilink join);

lsmeans Nrate*doy/pdiff;

contrast "DOY 25 June vs Aug. 12" DOY 1 0 -1 0;

contrast "DOY Aug. 12 vs Aug. 31" DOY 0 0 1 -1;

run;

ods graphics off;

ods html close;

proc sort data=lai10; by Nrate doy;

proc means data=lai10 mean noprint; by Nrate doy; var lai intpar;

output out=lai10mean mean=lai intpar; run; proc print data=lai10mean; run;

Biomass yields-Ch.4, Table 4.4, Table 4.5. 

options ls=150 ps=150 nodate nocenter formdlim="-";

data biom10;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2010.csv'

dlm="," firstobs=7;

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

drop Nratelbs tons comments;

*if env ="Urbana 2010" and block=1 and hybrid="TX09007" then biomass=.; *poor planting in 106-110 

and some flooding;

if env ="Dixon Springs 2010" and plot=301 then biomass=.; *no second harvest;

*if env ="Perry 2010" and plot=401 then biomass=.;

early=summer*2.24;
163

late=fall*2.24;

run; proc print; run;

proc sort data=biom10; by env type hybrid block Nrate; run; proc print;

*means by cutting and total biomass production;

proc sort data=biom10; by env type Nrate; run;

proc means data=biom10 noprint mean; var early; by env type Nrate;

output out=summer mean=summerbiom; run; proc print data=summer; run;

data summer; set summer;

drop _TYPE_ _FREQ_; run;

proc means data=biom10 noprint mean; var late; by env type Nrate;

output out=fall mean=fallbiom; run; proc print data=fall; run;

data fall; set fall; 

drop _TYPE_ _FREQ_; run;

data all; merge summer fall;

by env type Nrate; run; proc print data=all; run;

data all1; set all; 

if summerbiom=. then summerbiom=0;

total=summerbiom+fallbiom; run;

proc print data=all1; run;

*modeling 2010 total dry biomass yields;

*blup estimate statements calculating dry biomass yield in each environment by type and Nrate;

ods html;

ods graphics on;

proc glimmix data=biom10 plots=all;

class env block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr ;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

*lsmeans type*Nrate/plot=meanplot(sliceby=type join);

estimate "DS energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1|env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 1;

estimate "DS energy 56"

intercept 1 type 1 0 Nrate 0 1 0 0 0 type*Nrate 0 1|env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 1;

estimate "DS energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1|env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 1;

estimate "DS energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1|env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 0 1;

estimate "DS energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1|env 1 0

env*type 1 0 0 0 0 0 env*type*Nrate 0 0 0 0 1;

estimate "DS forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrate 0 0 0 0 0 1|env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 1;

estimate "DS forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1|env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 1;

estimate "DS forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1|env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 1;

estimate "DS forage 168"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nrate 0 0 0 0 0 0 0 0 1|env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 1;

estimate "DS forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1|env 1 0

env*type 0 1 0 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "PE energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1|env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE energy 56"

intercept 1 type 1 0 Nrate 0 1 0 0 0 type*Nrate 0 1|env 0 1
164

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1|env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1|env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1|env 0 1

env*type 0 0 1 0 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrate 0 0 0 0 0 1|env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1|env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1|env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE forage 168"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nrate 0 0 0 0 0 0 0 0 1|env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "PE forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1|env 0 1

env*type 0 0 0 1 0 0 env*type*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR energy 0"

intercept 1 type 1 0 Nrate 1 0 0 0 0 type*Nrate 1|env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR energy 56"

intercept 1 type 1 0 Nrate 0 1 0 0 0 type*Nrate 0 1|env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR energy 112"

intercept 1 type 1 0 Nrate 0 0 1 0 0 type*Nrate 0 0 1|env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR energy 168"

intercept 1 type 1 0 Nrate 0 0 0 1 0 type*Nrate 0 0 0 1|env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR energy 224"

intercept 1 type 1 0 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 1 0 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR forage 0"

intercept 1 type 0 1 Nrate 1 0 0 0 0 type*Nrate 0 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR forage 56"

intercept 1 type 0 1 Nrate 0 1 0 0 0 type*Nrate 0 0 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR forage 112"

intercept 1 type 0 1 Nrate 0 0 1 0 0 type*Nrate 0 0 0 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR forage 168"

intercept 1 type 0 1 Nrate 0 0 0 1 0 type*Nrate 0 0 0 0 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR forage 224"

intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1|env 0 0 1

env*type 0 0 0 0 0 1 env*type*Nrate 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "N 0 vs 56" Nrate 1 -1 0 0 0;

estimate "N 0 vs 112" Nrate 1 0 -1 0 0;

estimate "N 0 vs 168" Nrate 1 0 0 -1 0;

estimate "N 0 vs 224" Nrate 1 0 0 0 -1;

estimate "N 56 vs 112" Nrate 0 1 -1 0 0;
165

estimate "N 56 vs 168" Nrate 0 1 0 -1 0;

estimate "N 56 vs 224" Nrate 0 1 0 0 -1;

estimate "N 112 vs 168" Nrate 0 0 1 -1 0;

estimate "N 112 vs 224" Nrate 0 0 1 0 -1;

estimate "N 168 vs 224" Nrate 0 0 0 1 -1;

estimate "energy 0" intercept 1 type 1 Nrate 1 type*Nrate 1;

estimate "energy 56" intercept 1 type 1 Nrate 0 1 type*Nrate 0 1;

estimate "energy 112" intercept 1 type 1 Nrate 0 0 1 type*Nrate 0 0 1;

estimate "energy 168" intercept 1 type 1 Nrate 0 0 0 1 type*Nrate 0 0 0 1;

estimate "energy 224" intercept 1 type 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 1;

estimate "forage 0" intercept 1 type 0 1 Nrate 1 type*Nrate 0 0 0 0 0 1;

estimate "forage 56" intercept 1 type 0 1 Nrate 0 1 type*Nrate 0 0 0 0 0 0 1;

estimate "forage 112" intercept 1 type 0 1 Nrate 0 0 1 type*Nrate 0 0 0 0 0 0 0 1;

estimate "forage 168" intercept 1 type 0 1 Nrate 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 1;

estimate "forage 224" intercept 1 type 0 1 Nrate 0 0 0 0 1 type*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "energy 0 vs 56" Nrate 1 -1 type*Nrate 1 -1;

estimate "energy 0 vs 112" Nrate 1 0 -1 type*Nrate 1 0 -1;

estimate "energy 0 vs 168" Nrate 1 0 0 -1 type*Nrate 1 0 0 -1;

estimate "energy 0 vs 224" Nrate 1 0 0 0 -1 type*Nrate 1 0 0 0 -1;

estimate "energy 56 vs 112" Nrate 0 1 -1 0 0 type*Nrate 0 1 -1 0 0;

estimate "energy 56 vs 168" Nrate 0 1 0 -1 0 type*Nrate 0 1 0 -1 0;

estimate "energy 56 vs 224" Nrate 0 1 0 0 -1 type*Nrate 0 1 0 0 -1;

estimate "energy 112 vs 168" Nrate 0 0 1 -1 0 type*Nrate 0 0 1 -1 0;

estimate "energy 112 vs 224" Nrate 0 0 1 0 -1 type*Nrate 0 0 1 0 -1;

estimate "energy 168 vs 224" Nrate 0 0 0 1 -1 type*Nrate 0 0 0 1 -1;

estimate "forage 0 vs 56" Nrate 1 -1 type*Nrate 0 0 0 0 0 1 -1;

estimate "forage 0 vs 112" Nrate 1 0 -1 type*Nrate 0 0 0 0 0 1 0 -1;

estimate "forage 0 vs 168" Nrate 1 0 0 -1 type*Nrate 0 0 0 0 0 1 0 0 -1;

estimate "forage 0 vs 224" Nrate 1 0 0 0 -1 type*Nrate 0 0 0 0 0 1 0 0 0 -1;

estimate "forage 56 vs 112" Nrate 0 1 -1 0 0 type*Nrate 0 0 0 0 0 0 1 -1 0 0;

estimate "forage 56 vs 168" Nrate 0 1 0 -1 0 type*Nrate 0 0 0 0 0 0 1 0 -1 0;

estimate "forage 56 vs 224" Nrate 0 1 0 0 -1 type*Nrate 0 0 0 0 0 0 1 0 0 -1;

estimate "forage 112 vs 168" Nrate 0 0 1 -1 0 type*Nrate 0 0 0 0 0 0 0 1 -1 0;

estimate "forage 112 vs 224" Nrate 0 0 1 0 -1 type*Nrate 0 0 0 0 0 0 0 1 0 -1;

estimate "forage 168 vs 224" Nrate 0 0 0 1 -1 type*Nrate 0 0 0 0 0 0 0 0 1 -1;

estimate "Dixon Springs 2010 type difference" type 1 -1|env 1 env*type 1 -1;

estimate "Perry 2010 type difference" type 1 -1|env 0 1 env*type 0 0 1 -1;

estimate "Urbana 2010 type difference" type 1 -1|env 0 0 1 env*type 0 0 0 0 1 -1;

estimate "DS vs PE energy vs. forage" type 1 -1|env 1 -1 0 env*type 1 -1 -1 1;

estimate "DS vs UR energy vs. forage" type 1 -1|env 1 0 -1 env*type 1 -1 0 0 -1 1;

estimate "PE vs UR energy vs. forage" type 1 -1|env 0 1 -1 env*type 0 0 1 -1 -1 1;

estimate "DS vs PE energy" |env 1 -1 0 env*type 1 0 -1 0 0 0;

estimate "PE vs UR energy" |env 0 1 -1 env*type 0 0 1 0 -1 0;

output out=out1 residual=resid predicted=pred;

run;

ods graphics off;

ods html close;

*checking outliers;

data out1out; set out1;

if resid le 5 then delete;

run; proc print data=out1out; run;

*removing outliers;

data new; set out1; 

if resid ge 10 then delete;

run; 

ods html;

ods graphics on;

proc glimmix data=new plots=all;

class env block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr ;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);
166

covtest glm/wald;

output out=out1 residual=residnew predicted=prednew;

run;

ods graphics off;

ods html close;

*Running the model without these outliers did not change the significance of any of the 

factors or parameter estimates in a way that would warrant leaving them out. The residuals 

looked better but the model did not change significantly so I will keep them in the model. 

*Based upon the results of the model only the effect of Nrate and Type are significant at 

the alpha=.1 level, ;

*2010 forage sorghum harvest 1, harvest 2 analysis;

data forage; set biom10;

if type = "Energy" then delete;

run; proc print data=forage; run;

*2010 summer forage sorghum harvest analysis;

ods html;

ods graphics on;

proc glimmix data=forage plots=all;

class env block hybrid Nrate;

model early=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr ;

random env block(env)env*hybrid hybrid*block(env) env*Nrate env*Nrate*hybrid;

covtest glm/wald;

lsmeans Nrate/pdiff;

estimate "DS 0" intercept 1 Nrate 1 0 0 0 0|env 1 0 0 env*Nrate 1;

estimate "DS 56" intercept 1 Nrate 0 1 0 0 0|env 1 0 0 env*Nrate 0 1;

estimate "DS 112" intercept 1 Nrate 0 0 1 0 0|env 1 0 0 env*Nrate 0 0 1;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 0|env 1 0 0 env*Nrate 0 0 0 1;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1|env 1 0 0 env*Nrate 0 0 0 0 1;

estimate "PE 0" intercept 1 Nrate 1 0 0 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 1;

estimate "PE 56" intercept 1 Nrate 0 1 0 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 1;

estimate "PE 112" intercept 1 Nrate 0 0 1 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 1;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 0 1;

estimate "UR 0" intercept 1 Nrate 1 0 0 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 56" intercept 1 Nrate 0 1 0 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 112" intercept 1 Nrate 0 0 1 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

1;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1;

run;

ods graphics off;

ods html close;

*2010 fall forage sorghum harvest analysis;

ods html;

ods graphics on;

proc glimmix data=forage plots=all;

class env block hybrid Nrate;

model late=hybrid Nrate hybrid*Nrate/dist=normal ddfm=kr ;

random env block(env)env*hybrid hybrid*block(env) env*Nrate env*Nrate*hybrid;

covtest glm/wald;

lsmeans Nrate/pdiff;

estimate "DS 0" intercept 1 Nrate 1 0 0 0 0|env 1 0 0 env*Nrate 1;

estimate "DS 56" intercept 1 Nrate 0 1 0 0 0|env 1 0 0 env*Nrate 0 1;

estimate "DS 112" intercept 1 Nrate 0 0 1 0 0|env 1 0 0 env*Nrate 0 0 1;

estimate "DS 168" intercept 1 Nrate 0 0 0 1 0|env 1 0 0 env*Nrate 0 0 0 1;

estimate "DS 224" intercept 1 Nrate 0 0 0 0 1|env 1 0 0 env*Nrate 0 0 0 0 1;

estimate "PE 0" intercept 1 Nrate 1 0 0 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 1;

estimate "PE 56" intercept 1 Nrate 0 1 0 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 1;

estimate "PE 112" intercept 1 Nrate 0 0 1 0 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 1;

estimate "PE 168" intercept 1 Nrate 0 0 0 1 0|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 1;

estimate "PE 224" intercept 1 Nrate 0 0 0 0 1|env 0 1 0 env*Nrate 0 0 0 0 0 0 0 0 0 1;
167

estimate "UR 0" intercept 1 Nrate 1 0 0 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 56" intercept 1 Nrate 0 1 0 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 112" intercept 1 Nrate 0 0 1 0 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "UR 168" intercept 1 Nrate 0 0 0 1 0|env 0 0 1 env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0

1;

estimate "UR 224" intercept 1 Nrate 0 0 0 0 1|env 0 0 1

env*Nrate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1;

estimate "graze all" intercept 1 hybrid 1 0;

estimate "graze. n. bale" intercept 1 hybrid 0 1;

estimate "graze all vs graze. n. bale" hybrid 1 -1;

run;

ods graphics off;

ods html close;

*2009 biomass data;

data biom09;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2009.csv'

dlm="," firstobs=5;

input env$ block type$ hybrid$ Nrate plot tons;

biomass=tons*2.24;

run; proc print; run;

proc sort data=biom09; by env type hybrid block Nrate; run; proc print;

proc sort data=biom09; by env type Nrate; run;

proc means data=biom09 noprint mean; var biomass; by env type Nrate;

output out=mean09 mean=biomass; run; proc print data=mean09; run;

ods graphics on;

proc glimmix data=biom09 plots=RESIDUALPANEL;

class block type hybrid Nrate;

model biomass=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type);

random block block*type block*hybrid(type);

covtest glm/wald;

*lsmeans type*Nrate/pdiff;

estimate "energy 0 vs 50" Nrate 1 -1 type*Nrate 1 -1;

estimate "energy 0 vs 100" Nrate 1 0 -1 type*Nrate 1 0 -1;

estimate "energy 0 vs 150" Nrate 1 0 0 -1 type*Nrate 1 0 0 -1;

estimate "energy 50 vs 100" Nrate 0 1 -1 0 type*Nrate 0 1 -1 0;

estimate "energy 50 vs 150" Nrate 0 1 0 -1 type*Nrate 0 1 0 -1;

estimate "energy 100 vs 150" Nrate 0 0 1 -1 type*Nrate 0 0 1 -1;

estimate "forage 0 vs 50" Nrate 1 -1 type*Nrate 0 0 0 0 1 -1;

estimate "forage 0 vs 100" Nrate 1 0 -1 type*Nrate 0 0 0 0 1 0 -1;

estimate "forage 0 vs 150" Nrate 1 0 0 -1 type*Nrate 0 0 0 0 1 0 0 -1;

estimate "forage 50 vs 100" Nrate 0 1 -1 0 type*Nrate 0 0 0 0 0 1 -1 0;

estimate "forage 50 vs 150" Nrate 0 1 0 -1 type*Nrate 0 0 0 0 0 1 0 -1;

estimate "forage 100 vs 150" Nrate 0 0 1 -1 type*Nrate 0 0 0 0 0 0 1 -1;

contrast "linear N rate" Nrate -3 -1 1 3;

contrast "linear N rate energy vs forage" type*Nrate -3 -1 1 3 3 1 -1 -3;

run;

ods graphics off;

Correlations among plant height, expanded leaf number, and biomass yield

*2010 energy sorghum biomass yields;

options ls=150 ps=150 nodate nocenter formdlim="-";

data biom10;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2010.csv'

dlm="," firstobs=7;
168

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

if type="Forage" then delete;

drop Nratelbs summer fall tons comments;

run; proc print data=biom10; run;

*2009 energy sorghum biomass data;

data biom09;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2009.csv'

dlm="," firstobs=5;

input env$ block type$ hybrid$ Nrate plot tons;

biomass=tons*2.24;

if type="Forage" then delete;

drop tons;

run; proc print data=biom09; run;

*2009 & 2010 leaf number and height;

data htleafno;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum ht-leafno 2009 & 2010 for correlations.csv'

dlm="," firstobs=6;

input env$ date$ doy block sample type$ hybrid$ Nrate Nratelbs plot leaf htcm GDD10 GDD11 

comment$;

htm=htcm/100;

if type="Forage" then delete;

drop date sample Nratelbs htcm GDD10 GDD11 comment;

run; proc print data=htleafno; run;

*Urbana 2009 end of season ht and leaf number;

data htleaf09; set htleafno;

if env ne "Urbana 2009" then delete;

if doy ne 280 then delete;

run; proc print; run;

proc sort data=htleaf09; by env block type hybrid Nrate plot; run;

proc means data=htleaf09 mean noprint; var leaf htm; by env block type hybrid Nrate plot;

output out=new09 mean=leaf htm; run; proc print data=new09; run;

data new09; set new09; 

drop _TYPE_ _FREQ_; run; proc print data=new09; run;

*2010 end of season ht and leaf number;

data htleaf10; set htleafno;

if env ="Urbana 2009" then delete;

if env="Urbana 2010" and doy ne 270 then delete;

if env="Dixon Springs 2010" and doy ne 277 then delete;

if env="Perry 2010" and doy ne 276 then delete; 

run; proc print; run;

proc sort data=htleaf10; by env block type hybrid Nrate plot; run;

proc means data=htleaf10 mean noprint; var leaf htm; by env block type hybrid Nrate plot;

output out=new10 mean=leaf htm; run; proc print data=new10; run;

data new10; set new10; 

drop _TYPE_ _FREQ_; run; proc print data=new10; run;

*merging 2009 info;

proc sort data=biom09; by env block type hybrid Nrate plot; run;

data all09; merge biom09 new09;

by env block type hybrid Nrate; run;

proc print data=all09; run;

*Correlations among biomass, leaf number, and height for Urbana 2009;
169

proc corr data=all09; 

var biomass leaf htm; run;

proc sgscatter data=all09;

title "Urbana 2009";

matrix htm leaf biomass/group=Nrate;

run;

*merging 2010 info;

proc sort data=biom10; by env block type hybrid Nrate plot; run;

data all10; merge biom10 new10;

by env block type hybrid Nrate; run;

proc print data=all10; run;

*Correlations among biomass, leaf number, and height for 2010;

proc corr data=all10; 

var biomass leaf htm; 

by env; run;

proc sgscatter data=all10;

title "2010 environments";

matrix htm leaf biomass/group=env;

run;
170

APPENDIX D

Selected SAS code for Ch. 5

Nitrogen response curves, EONR-Ch.5, Fig. 5.1, Table 5.1.

options ls=150 ps=150 nodate nocenter formdlim="-";

data biom10;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2010.csv'

dlm="," firstobs=7;

input env$ block type$ hybrid$ Nratelbs Nrate plot summer fall tons biomass comments$;

drop Nratelbs summer fall tons comments;

run; proc print; run;

proc sort data=biom10; by env type hybrid block Nrate; run; proc print;

*URBANA 2010 ENERGY SORGHUM;

data urbenergy; set biom10; 

if type ne "Energy" then delete;

if env ne "Urbana 2010" then delete;

*if block=1 and hybrid="TX09007" then delete; *poor planting in 106-110 and some flooding;

X=Nrate;

X2=X*X;

run; proc print data=urbenergy; run; 

proc sort data=urbenergy; by env hybrid block type Nrate; 

proc print data=urbenergy; run;

proc sort data=urbenergy; by block; run; proc print;

proc nlmixed data=urbenergy;

parms alpha=22.5 beta=.075 gamma=-.0001 /*s2blk=1*/ s2e=45;

x0 = -.5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0)/* + blk */;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=((.87/35)-beta)/(2*gamma);

EONR2a=((.87/50)-beta)/(2*gamma);

EONR3a=((.87/65)-beta)/(2*gamma);

EONR1b=((1.07/35)-beta)/(2*gamma);

EONR2b=((1.07/50)-beta)/(2*gamma);

EONR3b=((1.07/65)-beta)/(2*gamma);

EONR1c=((1.32/35)-beta)/(2*gamma);

EONR2c=((1.32/50)-beta)/(2*gamma);

EONR3c=((1.32/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response1;

run;

proc print data=response1; run;

data response1; set response1;

residuals=biomass-pred; run; proc print data=response1; run;

data ce;

do i=0 to 224 by 0.5; 

do n=187.42;

yielde = (i <n)*(22.3700+0.06470*i -0.00017*i*i) +
171

(i>=n)*(22.3700+0.06470*n -0.00017*n*n);

output;

end;

end;

proc print data=ce; run;

*URBANA 2010 FORAGE SORGHUM;

data urbforage; set biom10; 

if type ne "Forage" then delete;

if env ne "Urbana 2010" then delete;

X=Nrate;

X2=X*X;

run; proc print data=urbforage; run; 

proc sort data=urbforage; by env hybrid block type Nrate; 

proc print data=urbforage; run;

proc sort data=urbforage; by block; run; proc print;

proc nlmixed data=urbforage;

parms alpha=16.25 beta=.049 gamma=-.0001 /*s2blk=1*/ s2e=45;

x0 = -.5*beta / gamma; *estimate of joint point;

y = (x <x0)*(alpha+beta*x +gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0) /* + blk */;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=((.87/35)-beta)/(2*gamma);

EONR2a=((.87/50)-beta)/(2*gamma);

EONR3a=((.87/65)-beta)/(2*gamma);

EONR1b=((1.07/35)-beta)/(2*gamma);

EONR2b=((1.07/50)-beta)/(2*gamma);

EONR3b=((1.07/65)-beta)/(2*gamma);

EONR1c=((1.32/35)-beta)/(2*gamma);

EONR2c=((1.32/50)-beta)/(2*gamma);

EONR3c=((1.32/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response2;

run;

proc print data=response2; run;

data response2; set response2;

residuals=biomass-pred; run; proc print data=response2; run;

data cf;

do j=0 to 224 by 0.5; 

do n=193.69;

yieldf = (j <n)*(16.2596+0.0496*j -0.00013*j*j) +

(j>=n)*(16.2596+0.0496*n -0.00013*n*n);

output;

end;

end;

proc print data=cf; run;

*PLOTTING RESPONSE CURVES FOR URBANA 2010;

data all; merge urbenergy urbforage; 

by env hybrid block type Nrate biomass X X2;

run; proc print data=all; run;

proc sort data=all; by type; run; proc print data=all; run;

data urb; merge all ce cf; run; proc print data=urb; run;
172

proc sort data=urb; by i j; run; proc print data=urb; run;

proc sgplot data=urb noautolegend;

title "Urbana 2010";

yaxis label="Biomass (Mg/ha)" values=(0 to 50 by 10);

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=biomass x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

series y=yielde x=i/lineattrs=(pattern=longdash thickness=2px color=blue) name="E"

legendlabel="Energy";

series y=yieldf x=j/lineattrs=(pattern=solid thickness=2px color=darkred) name="F"

legendlabel="Forage";

discretelegend "obs" "E" "F"/across=1

position=topleft location=inside;

run;

*plotting residuals;

data Urb10; merge response1 response2; by type; 

run; proc print data=Urb10; run;

proc sgplot data=Urb10 noautolegend;

title "Urbana 2010";

yaxis label="Residuals (observed yield- predicted yield)";

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=residuals x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

refline 0/axis=y;

run;

*DIXON SPRINGS 2010 ENERGY SORGHUM;

data dsenergy; set biom10; 

if type ne "Energy" then delete;

if env ne "Dixon Springs 2010" then delete;

*if plot=311 then delete;

*if plot=109 then delete;

*if plot=204 then delete;

X=Nrate;

X2=X*X;

run; proc print data=dsenergy; run; 

proc sort data=dsenergy; by env hybrid block type Nrate; 

proc print data=dsenergy; run;

proc sort data=dsenergy; by block; run; proc print;

proc nlmixed data=dsenergy;

parms alpha=17 beta=.18 gamma=-.00247 /*s2blk=1*/ s2e=8.5;

x0 = -.5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0)/* + blk */;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=((.87/35)-beta)/(2*gamma);

EONR2a=((.87/50)-beta)/(2*gamma);

EONR3a=((.87/65)-beta)/(2*gamma);

EONR1b=((1.07/35)-beta)/(2*gamma);

EONR2b=((1.07/50)-beta)/(2*gamma);

EONR3b=((1.07/65)-beta)/(2*gamma);

EONR1c=((1.32/35)-beta)/(2*gamma);

EONR2c=((1.32/50)-beta)/(2*gamma);

EONR3c=((1.32/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;
173

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response3;

run;

proc print data=response3; run;

data response3; set response3;

residuals=biomass-pred; run; proc print data=response3; run;

data ce;

do i=0 to 224 by 0.5; 

do n=41.7536;

yielde = (i <n)*(17.6916+0.1702*i -0.00204*i*i) +

(i>=n)*(17.6916+0.1702*n -0.00204*n*n);

output;

end;

end;

proc print data=ce; run;

*DIXON SPRINGS 2010 FORAGE SORGHUM;

data dsforage; set biom10; 

if type ne "Forage" then delete;

if env ne "Dixon Springs 2010" then delete;

if plot=301 then delete; *no second harvest;

*if plot=114 then delete;

*if plot=115 then delete;

*if plot=306 then delete;

*if plot=403 then delete;

X=Nrate;

X2=X*X;

run; proc print data=dsforage; run; 

proc sort data=dsforage; by env hybrid block type Nrate; 

proc print data=dsforage; run;

proc sort data=dsforage; by block; run; proc print;

proc nlmixed data=dsforage;

parms alpha=16 beta=.09 gamma=-.0008 /*s2blk=1*/ s2e=3.5;

x0 = -.5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0)/* + blk */;

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1a=((.87/35)-beta)/(2*gamma);

EONR2a=((.87/50)-beta)/(2*gamma);

EONR3a=((.87/65)-beta)/(2*gamma);

EONR1b=((1.07/35)-beta)/(2*gamma);

EONR2b=((1.07/50)-beta)/(2*gamma);

EONR3b=((1.07/65)-beta)/(2*gamma);

EONR1c=((1.32/35)-beta)/(2*gamma);

EONR2c=((1.32/50)-beta)/(2*gamma);

EONR3c=((1.32/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response4;

run;

proc print data=response4; run;

data response4; set response4;

residuals=biomass-pred; run; proc print data=response4; run;
174

data cf;

do j=0 to 224 by 0.5; 

do n=47.8261;

yieldf = (j <n)*(16.6889+0.06675*j -0.00070*j*j) +

(j>=n)*(16.6889+0.06675*n -0.00070*n*n);

output;

end;

end;

proc print data=cf; run;

*PLOTTING RESPONSE CURVES FOR Dixon springs 2010;

data allds; merge dsenergy dsforage; 

by env hybrid block type Nrate biomass X X2;

run; proc print data=allds; run;

proc sort data=allds; by type; run; proc print data=allds; run;

data ds; merge allds ce cf; run; proc print data=ds; run;

proc sort data=ds; by i j; run; proc print data=ds; run;

proc sgplot data=ds noautolegend;

title "Dixon Springs 2010";

yaxis label="Biomass (Mg/ha)" values=(0 to 50 by 10);

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=biomass x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

series y=yielde x=i/lineattrs=(pattern=longdash thickness=2px color=blue) name="E"

legendlabel="Energy";

series y=yieldf x=j/lineattrs=(pattern=solid thickness=2px color=darkred) name="F"

legendlabel="Forage";

discretelegend "obs" "E" "F"/across=1

position=topleft location=inside;

run;

*plotting residuals;

data DS10; merge response3 response4; by type; 

run; proc print data=DS10; run;

proc sgplot data=DS10 noautolegend;

title "Dixon Springs 2010";

yaxis label="Residuals (observed yield- predicted yield)";

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=residuals x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

refline 0/axis=y;

run;

*PERRY 2010 ENERGY SORGHUM;

data perenergy; set biom10; 

if type ne "Energy" then delete;

if env ne "Perry 2010" then delete;

*if plot=310 then delete;

*if plot=110 then delete;

*if plot=104 then delete;

*if plot=206 then delete;

*if plot=301 then delete;

*if plot=420 then delete;

*if plot=108 then delete;

*if plot=309 then delete;

X=Nrate;

X2=X*X;

run; proc print data=perenergy; run; 

proc sort data=perenergy; by env hybrid block type Nrate; 

proc print data=perenergy; run;

/*

proc sort data=perenergy; by block; run; proc print;
175

proc nlmixed data=perenergy;

parms alpha=18 beta=.01 gamma=-.0001 s2e=10;

x0 = -.5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0);

plateau =alpha + beta*x0 + gamma*x0*x0;

EONR1=((1.1638/35)-beta)/(2*gamma);

EONR2=((1.1638/50)-beta)/(2*gamma);

EONR3=((1.1638/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35" EONR1;

estimate "$50" EONR2;

estimate "$65" EONR3;

predict y out=response;

run;

proc print data=response; run;

data ce;

do i=0 to 224 by 0.5; 

do n=41.7536;

yielde = (i <n)*(17.6916+0.1702*i -0.00204*i*i) +

(i>=n)*(17.6916+0.1702*n -0.00204*n*n);

output;

end;

end;

proc print data=ce; run;

*/

*running it as a linear equation;

proc sort data=perenergy; by block; run; proc print;

proc nlmixed data=perenergy;

parms alpha=28 beta=.03 s2e=42;

y=alpha + beta*x;

model biomass ~ normal(y, s2e);

predict y out=response5;

run;

proc print data=response5; run;

data response5; set response5;

residuals=biomass-pred; run; proc print data=response5; run;

data ce;

do i=0 to 224 by 0.5; 

yielde = 27.1121+0.04319*i;

output;

end;

proc print data=ce; run;

*/

*Perry 2010 FORAGE SORGHUM;

data perforage; set biom10; 

if type ne "Forage" then delete;

if env ne "Perry 2010" then delete;

*if plot=401 then delete; *extremely high for 0 N, something not correct;

X=Nrate;

X2=X*X;

run; proc print data=perforage; run; 

proc sort data=perforage; by env hybrid block type Nrate; 

proc print data=perforage; run;

proc sort data=perforage; by block; run; proc print;

proc nlmixed data=perforage;

parms alpha=14 beta=.05 gamma=-.0001 /*s2blk=1*/ s2e=9;

x0 = -.5*beta / gamma;

y = (x <x0)*(alpha+beta*x + gamma*x*x) + (x>=x0)*(alpha+beta*x0+gamma*x0*x0)/* + blk */;

plateau =alpha + beta*x0 + gamma*x0*x0;
176

EONR1a=((.87/35)-beta)/(2*gamma);

EONR2a=((.87/50)-beta)/(2*gamma);

EONR3a=((.87/65)-beta)/(2*gamma);

EONR1b=((1.07/35)-beta)/(2*gamma);

EONR2b=((1.07/50)-beta)/(2*gamma);

EONR3b=((1.07/65)-beta)/(2*gamma);

EONR1c=((1.32/35)-beta)/(2*gamma);

EONR2c=((1.32/50)-beta)/(2*gamma);

EONR3c=((1.32/65)-beta)/(2*gamma);

model biomass ~ normal(y,s2e);

*random blk ~ NORMAL(0,s2blk) SUBJECT=block;

estimate 'X0' X0;

estimate 'plateau' plateau; 

estimate "$35a" EONR1a;

estimate "$50a" EONR2a;

estimate "$65a" EONR3a;

estimate "$35b" EONR1b;

estimate "$50b" EONR2b;

estimate "$65b" EONR3b;

estimate "$35c" EONR1c;

estimate "$50c" EONR2c;

estimate "$65c" EONR3c;

predict y out=response6;

run;

proc print data=response6; run;

data response6; set response6;

residuals=biomass-pred; run; proc print data=response6; run;

data cf;

do j=0 to 224 by 0.5; 

do n=214.88;

yieldf = (j <n)*(14.1348+0.03497*j -0.00008*j*j) +

(j>=n)*(14.1348+0.03497*n -0.00008*n*n);

output;

end;

end;

proc print data=cf; run;

*PLOTTING RESPONSE CURVES FOR Perry 2010;

data allper; merge perenergy perforage; 

by env hybrid block type Nrate plot biomass X X2;

run; proc print data=allper; run;

proc sort data=allper; by type; run; proc print data=allper; run;

data per; merge allper ce cf; run; proc print data=per; run;

proc sort data=per; by i j; run; proc print data=per; run; 

proc sgplot data=per noautolegend;

title "Perry 2010";

yaxis label="Biomass (Mg/ha)" values=(0 to 50 by 10);

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=biomass x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

series y=yielde x=i/lineattrs=(pattern=longdash thickness=2px color=blue) name="E"

legendlabel="Energy";

series y=yieldf x=j/lineattrs=(pattern=solid thickness=2px color=darkred) name="F"

legendlabel="Forage";

discretelegend "obs" "E" "F"/across=1

position=topleft location=inside;

run;

*plotting residuals;

data PE10; merge response5 response6; by type; 

run; proc print data=PE10; run;

proc sgplot data=PE10 noautolegend;

title "Perry 2010";

yaxis label="Residuals (observed yield- predicted yield)";
177

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=residuals x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

refline 0/axis=y;

run;

*2009 biomass data;

data biom09;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

manuscript 1\sorghum dry biomass yield 2009.csv'

dlm="," firstobs=5;

input env$ block type$ hybrid$ Nrate plot tons;

biomass=tons*2.24;

run; proc print; run;

proc sort data=biom09; by env type hybrid block Nrate; run; proc print;

*Urbana 2009 ENERGY SORGHUM;

data urbenergy; set biom09; 

if type ne "Energy" then delete;

X=Nrate;

X2=X*X;

run; proc print data=urbenergy; run; 

proc sort data=urbenergy; by env hybrid block type Nrate; 

proc print data=urbenergy; run;

*running it as a linear equation;

proc sort data=urbenergy; by block; run; proc print;

proc nlmixed data=urbenergy;

parms alpha=13 beta=.07 s2e=22;

y=alpha + beta*x;

model biomass ~ normal(y, s2e);

predict y out=response7;

run;

proc print data=response7; run;

data response7; set response7;

residuals=biomass-pred; run; proc print data=response7; run;

data ce;

do i=0 to 150 by 0.5; 

yielde = 12.7240+0.07618*i;

output;

end;

proc print data=ce; run;

*Urbana 2009 FORAGE SORGHUM;

data urbforage; set biom09; 

if type ne "Forage" then delete;

X=Nrate;

X2=X*X;

run; proc print data=urbforage; run; 

proc sort data=urbforage; by env hybrid block type Nrate; 

proc print data=urbforage; run;

*running it as a linear equation;

proc sort data=urbforage; by block; run; proc print;

proc nlmixed data=urbforage;

parms alpha=7 beta=.07 s2e=5;

y=alpha + beta*x;
178

model biomass ~ normal(y, s2e);

predict y out=response8;

run;

proc print data=response8; run;

data response8; set response8;

residuals=biomass-pred; run; proc print data=response8; run;

data cf;

do j=0 to 150 by 0.5; 

yieldf = 6.8105+0.06752*j;

output;

end;

proc print data=cf; run;

*PLOTTING CURVES FOR Urbana 2009;

data allur; merge urbenergy urbforage; 

by env hybrid block type Nrate plot biomass X X2;

run; proc print data=allur; run;

proc sort data=allur; by type; run; proc print data=allur; run;

data ur; merge allur ce cf; run; proc print data=ur; run;

proc sort data=ur; by i j; run; proc print data=ur; run; 

proc sgplot data=ur noautolegend;

title "Urbana 2009";

yaxis label="Biomass (Mg/ha)" values=(0 to 50 by 10);

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=biomass x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

series y=yielde x=i/lineattrs=(pattern=longdash thickness=2px color=blue) name="E"

legendlabel="Energy";

series y=yieldf x=j/lineattrs=(pattern=solid thickness=2px color=darkred) name="F"

legendlabel="Forage";

discretelegend "obs" "E" "F"/across=1

position=topleft location=inside;

run;

*plotting residuals;

data UR09; merge response7 response8; by type; 

run; proc print data=UR10; run;

proc sgplot data=UR09 noautolegend;

title "Urbana 2009";

yaxis label="Residuals (observed yield- predicted yield)";

xaxis label= "N rate (kg/ha)" values=(0 to 225 by 25);

scatter y=residuals x=x/group=type markerattrs=(size=8) name="obs" NOMISSINGGROUP;

refline 0/axis=y;

run;

SPAD, leaf TN, and leaf TC-Ch.5, Table 5.2, Table 5.3.

*SPAD, leaf TN, leaf TC;

options ls=150 ps=150 nodate nocenter formdlim="-";

data TN;

length env$ 18;

length date$ 16; 

length hybrid$ 14;

length time$ 12;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

man. 2\2009 2010 TN TC SPAD.csv'

dlm="," firstobs=14;

input env$ date$ doy time$ block sample type$ hybrid$ Nrate plot TN TC SPAD SLA;

drop date sample plot doy;

*if env ="Urbana 2009" and doy=232 then delete;

run; proc print; run;

proc sort data=TN; by env time type hybrid block Nrate SLA; run; proc print;

proc sort data=TN; by env time type hybrid Nrate;
179

proc means data=TN noprint mean std; var TN TC SPAD SLA; by env time type hybrid Nrate;

output out=means mean=mean_TN mean_TC mean_SPAD mean_SLA; run; proc print data=means; run;

*URBANA 2009;

data Urb2009; set TN; 

if env ne "Urbana 2009" then delete;

if time = "early-summer" then time=1;

if time = "mid-summer" then time=2;

if time = "late-summer" then time=3;

run; proc sort data=Urb2009; by env time type hybrid Nrate; run; proc print data=Urb2009; run;

*SPAD;

ods html;

ods graphics on;

proc glimmix data=Urb2009 plots=residualpanel;

class block type hybrid Nrate time;

model SPAD=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type) time type*time time*hybrid(type) 

Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal ddfm=kr;

random block block*type block*hybrid(type) block*Nrate*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate*time/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -3 -1 1 3;

estimate "quad N" Nrate 1 -1 -1 1;

lsmeans time*type*Nrate/pdiff plot=meanplot(sliceby=type*time join ilink);

run;

ods graphics off;

ods html close;

proc corr data=Urb2009;

var SPAD Nrate; run;

data TotalN; set Urb2009;

if time = 1 then delete; *this run for total N is mest up;

run; proc print data=totalN; run;

proc corr data=totalN;

var SPAD TN Nrate; run;

*TN;

ods html;

ods graphics on;

proc glimmix data=TotalN plots=residualpanel;

class block type hybrid Nrate time;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type) time type*time time*hybrid(type) 

Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal ddfm=kr;

random block block*type block*hybrid(type) block*Nrate*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate*time/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -3 -1 1 3;

estimate "quad N" Nrate 1 -1 -1 1;

run;

ods graphics off;

ods html close;

*TC;

ods html;

ods graphics on;

proc glimmix data=TotalN plots=residualpanel;

class block type hybrid Nrate time;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type) time type*time time*hybrid(type) 

Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal ddfm=kr;

random block block*type block*hybrid(type) block*Nrate*hybrid(type);

covtest glm/wald;
180

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate*time/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -3 -1 1 3;

estimate "quad N" Nrate 1 -1 -1 1;

run;

ods graphics off;

ods html close;

*URBANA 2010;

data Urb2010; set TN; 

if env ne "Urbana 2010" then delete;

run; proc print data=Urb2010; run;

proc corr data=Urb2010;

var SPAD TN Nrate; run;

*Dixon Springs 2010;

data DS2010; set TN; 

if env ne "Dixon Springs 2010" then delete;

run; proc print data=DS2010; run;

proc corr data=DS2010;

var SPAD TN; run;

*Perry 2010;

data PE2010; set TN; 

if env ne "Perry 2010" then delete;

run; proc print data=PE2010; run;

proc corr data=PE2010;

var SPAD TN; run;

data data2010; set TN; 

if env="Urbana 2009" then delete;

run; proc print data=data2010; run;

*SPAD;

ods html;

ods graphics on;

proc glimmix data=data2010 plots=residualpanel;

class env block type hybrid Nrate time;

model spad=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal 

ddfm=kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time 

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -2 -1 0 1 2;

estimate "quad N" Nrate 2 -1 -2 -1 2;

run;

ods html;

ods graphics on;

*TN;

ods html;

ods graphics on;

proc glimmix data=data2010 plots=residualpanel;

class env block type hybrid Nrate time;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)
181

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal 

ddfm=kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time 

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -2 -1 0 1 2;

estimate "quad N" Nrate 2 -1 -2 -1 2; 

run;

ods html;

ods graphics on;

*TC;

ods html;

ods graphics on;

proc glimmix data=data2010 plots=residualpanel;

class env block type hybrid Nrate time;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)

time type*time time*hybrid(type) Nrate*time type*Nrate*time Nrate*time*hybrid(type)/dist=normal 

ddfm=kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type) block*hybrid*Nrate(env type)

env*time env*type*time env*time*hybrid(type) env*Nrate*time env*type*Nrate*time 

env*Nrate*time*hybrid(type);

covtest glm/wald;

lsmeans time*hybrid(type)/pdiff;

lsmeans time*type/pdiff;

lsmeans Nrate/pdiff;

estimate "linear N" Nrate -2 -1 0 1 2;

estimate "quad N" Nrate 2 -1 -2 -1 2; 

run;

ods html;

ods graphics on;

*early-summer correlations;

data early; set data2010;

if time="mid-summer" then delete;

run; proc print data=early; run;

proc corr data=early;

var SPAD TN Nrate; run;

*mid-summer correlations;

data mid; set data2010;

if time="early-summer" then delete;

run; proc print data=mid; run;

proc corr data=mid;

var SPAD TN Nrate; run;

Harvest TN and harvest TC-Ch.5, Table 5.4.

options ls=150 ps=150 nodate nocenter formdlim="-";

data harvest;

length env$ 18;

length hybrid$ 14;

infile 'C:\Users\maughan2\Documents\My Dropbox\sorghum fertility trial\2009 & 2010 analysis for 

man. 2\2009 2010 TN TC harvest samples.csv'

dlm="," firstobs=9;

input env$ block type$ hybrid$ Nrate plot position wt pos wt TN TC;

drop plot position wt pos;

run; proc print; run;
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*URBANA 2009;

data Urbana2009; set harvest; 

if env ne "Urbana 2009" then delete;

run; proc print data=Urbana2009; run;

*TN;

ods html;

ods graphics on;

proc glimmix data=Urbana2009 plots=residualpanel;

class block type hybrid Nrate;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr;

random block block*type block*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/pdiff;

lsmeans Nrate/pdiff;

run;

ods graphics off;

ods html close;

*TC;

ods html;

ods graphics on;

proc glimmix data=Urbana2009 plots=residualpanel;

class block type hybrid Nrate;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr;

random block block*type block*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/pdiff;

lsmeans Nrate/pdiff;

run;

ods graphics off;

ods html close;

*2010;

data envs2010; set harvest; 

if env = "Urbana 2009" then delete;

run; proc print data=envs2010; run;

*TN;

ods html;

ods graphics on;

proc glimmix data=envs2010 plots=residualpanel;

class env block type hybrid Nrate;

model TN=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/pdiff;

lsmeans Nrate/pdiff;

run;

ods graphics off;

ods html close;

*TC;

ods html;

ods graphics on;

proc glimmix data=envs2010 plots=residualpanel;

class env block type hybrid Nrate;

model TC=type hybrid(type) Nrate type*Nrate Nrate*hybrid(type)/dist=normal ddfm=kr;

random env block(env) env*type type*block(env) env*hybrid(type) block*hybrid(env type) env*Nrate

env*type*Nrate env*Nrate*hybrid(type);

covtest glm/wald;

lsmeans type*Nrate/pdiff;

lsmeans Nrate/pdiff;

run;

ods graphics off;

ods html close;
183

CURRICULUM VITAE

Matthew W. Maughan, Ph.D. candidate

Education

University of Illinois Urbana-Champaign Urbana, IL

Ph.D. in Crop Sciences, summer 2011 (expected)

University of Illinois Urbana-Champaign Urbana, IL

M.S. in Crop Sciences, May 2008

Brigham Young University-Idaho Rexburg, ID

B.S. in Agricultural Business Science & Technology, May 2006

Work Experience

Research Assistant (while Ph.D. student) Aug. 2008 – present

Department Crop Sciences, University of Illinois Urbana-Champaign

Supervised by Dr. Germán Bollero, Dr. D.K. Lee and Dr. Tom Voigt.

Major research projects:

•Meta-analysis of switchgrass: management and environmental factors that affect 

biomass yields in the US and southern Canada.

•Field experimentation of Miscanthus x giganteus productivity in Nebraska, Illinois, 

Kentucky, and New Jersey, and the impacts of nitrogen rate, season of growth, and 

environmental conditions on morphology, plant growth, and biomass yield.

•Management of biomass sorghum hybrids for biomass feedstock production under 

different nitrogen rates in four Illinois environments.

Research Assistant (while M.S. student) Aug. 2006 – May 2008

Department Crop Sciences, University of Illinois Urbana-Champaign

Supervised by Dr. Germán Bollero and Dr. Benjamin Tracy.

Replicated farm scale study investigating the impact of an integrated crop-livestock 

system that includes perennial pastures and winter cover crops on soil physical and 

biological properties, and corn grain yield. 

Teaching Assistant 2007-2010

Supervised by Dr. Germán Bollero and Dr. Donald Bullock. 

Teaching assistant for following courses:

1) Applied Statistical Methods I-Introductory Applied Statistics (Fall 2007/Spring 

2008/Fall 2008).

2) Applied Statistical Methods II-Experimental Design and Analysis (Spring 

2009/Spring 2010). 

3) Regression Analysis (Fall 2009).

Responsibilities for these three courses included:
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 1) Preparing teaching material used in weekly laboratory sessions where I taught and 

reviewed lecture material, and provided statistical examples applicable to 

assigned homework, exams, and quizzes. 

 2) Taught procedures and coding in Statistical Analysis Software (SAS). 

 3) Administered and graded scheduled quizzes, graded homework assignments, and 

helped to grade midterms and final exam.

 4) Held weekly office hours, and interacted and taught students through Web CT 

(Compass).

Farm Hand/Supervisor, Maughan Farms, Inc. 1996-2006

Maughan Farms Inc. is a seed potato, corn, small grain, and hay farm near Fort Benton, 

MT. This is a family farm operation on which I have worked at different times between 

1996 and 2006. Responsibilities and tasks related to this farm business have included:

•Planting and harvesting of seed potatoes, wheat, and corn.

•Fertilizer and chemical application.

•Center pivot, wheel line, and hand line irrigation of crops

•Solid set irrigation of potatoes on corners of center pivots. 

•Supervision and participation in potato rogueing crew for disease identification and 

removal.

•Wheat rogueing, primarily for wild oat and rye. 

•Haying, raking, baling, and stacking hay. 

•Extensive field and farm equipment operation including tractors, implements, and 

harvesters. 

•Various other jobs and responsibilities included: feeding and tending livestock, and 

light mechanical work on farm equipment (i.e. oil changes and general maintenance).

Publications, peer reviewed

Tracy, B.F., M. Maughan, N. Post, D.B. Faulkner. 2010. Integrating Annual and 

Perennial Warm-season Grasses in a Temperate Grazing System. Crop Sci. 50: 2171-2177.

Maughan, M.W., G. Bollero, J.P.C. Flores, B.F. Tracy, and F.G. Fernández. 2009. Soil 

Quality and Corn Yield under Crop–Livestock Integration in Illinois. Agron. J. 101:1511-

1521.

Anderson, E., R. Arundale, M. Maughan, A.Oladeinde, A. Wycislo, and T. Voigt. 

Growth and Agronomy of Miscanthus x giganteus for Biomass Production in the US. 

Biofuels 2:167-183.
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Manuscripts in preparation or submitted

Maughan, M., G. Bollero, D.K. Lee, R. Darmody, S. Bonos, L. Cortese, J. Murphy, R. 

Gaussoin, M. Sousek, D. Williams, L. Williams, F. Miguez, and T. Voigt. 2010. Miscanthus

x giganteus Productivity: The Effects of Management in Different Environments. GCB 

Bioenergy (in review). 

Maughan, M.W., G. Bollero, J.P.C. Flores, B.F. Tracy, and F.G. Fernández. Multivariate 

Analysis of Soil Properties in an Integrated Crop–Livestock System in Illinois (in 

preparation).

Maughan, M., F. Miguez, G. Bollero, D. Wang, T. Voigt, and D.K. Lee. Management 

and environmental factors that affect switchgrass biomass production. (in 

preparation).

Maughan, M., A. Parrish, G. Bollero, T. Voigt, D. Bullock, W. Rooney, and D.K. Lee. 

Management of forage and energy sorghum for biomass production in Illinois. (in 

preparation).

Invited Presentations

Maughan, M.W. 2011. Miscanthus x giganteus Productivity: The Effects of Management 

in Different Environments. Eighth annual Bioenergy Feedstock Symposium, Jan 11-12, 

2011. Champaign, IL. 

Maughan, M.W. 2008. Soil properties and corn yield response in the management of an 

integrated crop-livestock system. Univ. of Illinois Dudley Smith Farm Beef Cow-Calf 

Field Day, June 2008. Pana, IL.

Maughan, M.W. 2007. Research on soil properties at the Dudley Smith farm. Univ. of 

Illinois Dudley Smith Farm Beef Cow-Calf Field Day, June 2007. Pana, IL.

Abstracts at Professional meetings

Maughan, M., D.K. Lee, A. Parrish, G. Bollero, and T. Voigt. 2010. Management of 

forage and energy sorghum in Illinois for biomass feedstock production. Long Beach, 

CA. In annual meeting abstracts. ASA-CSSA-SSSA, Madison, WI.

Maughan, M., F. Miguez, T. Voigt, S. Bonos, L. Cortese, J. Murphy, R. Gaussoin, M. 

Sousek, D. Williams, and G. Bollero. 2010. Field experimentation of Miscanthus x 

giganteus growth in KY, NE, and NJ, and validation of biomass crop model, BioCro. 

Long Beach, CA. In annual meeting abstracts. ASA-CSSA-SSSA, Madison, WI.
186

Maughan, M., F. Miguez, G. Bollero, D.K. Lee, and T. Voigt. 2009. Meta-analysis of 

Switchgrass: Management and Environmental Factors that Affect Biomass Production. 

Pittsburgh, PA. In annual meeting abstracts. ASA-CSSA-SSSA, Madison, WI. 

Maughan, M., F. Miguez, T. Voigt, S. Bonos, J. Murphy, R. Gaussoin, D. Williams, and 

G. Bollero. 2009. Miscanthus x giganteus growth and survival in IL, IN, KY, NE, and NJ. 

Pittsburgh, PA. In annual meeting abstracts. ASA-CSSA-SSSA, Madison, WI.

Maughan, M.W., G.A. Bollero, and B.F. Tracy. 2007. Integrated crop-livestock system: 

effect on soil compaction, microbial biomass, and grain yield. New Orleans, LA. In 

annual meeting abstracts. ASA-CSSA-SSSA. Madison, WI.

Awards/Fellowships

•2009-2010 Lawrence E. Schrader and Elfriede Massier Plant Physiology Fellowship, 

Dept. of Crop Sciences, Univ. of Illinois.

•2008-2009 University Fellowship, Block Grant, Univ. of Illinois. 

•Recognized at the Univ. of Illinois on the “Incomplete List of Teachers Ranked as 

Excellent by their Students” during 4 semesters (Fall 2007, Spring 2009, Fall 2009, 

Spring 2010).

•2006 Rutger-Harper Walker Agriculture Leadership Award, BYU-Idaho.

•2005 Outstanding Agricultural Science Award & Grant, BYU-Idaho.

•Eagle Scout Award. 2000. 

Leadership/Service

•Crop Science Graduate Organization, Vice President (2009-2010), and Treasurer (2007-

2009), Dept. of Crop Sciences, Univ. of Illinois.

•Boy Scout leader (2006-2010).Varsity team 333. Prairielands Council, Champaign, IL.

International Experience & Languages

•Citizen Ambassador (October 9-19, 2009). Participated in an Agronomy Delegation 

Trip to the People’s Republic of China representing the Agronomy Society of America 

to participate in bicultural exchanges and discussions with Chinese Agronomists and 

Ag Professionals regarding various aspects of Chinese agriculture including soils, crop 

geography, and agricultural production practices.

•Lived and served in Brazil for 2 years (2001-2003) as a volunteer missionary, becoming 

immersed in Brazilian culture and the Portuguese language.

•Bilingual-Portuguese. Speaking & Conversation – advanced level. Reading & Writing –

intermediate level.
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Computer and statistical software skills

Microsoft office (Word, Excel, PowerPoint)

Statistical Analysis Software (SAS)

R-software for statistical computing

SigmaPlot-data analysis and graphing software

Professional & Honor Societies

American Society of Agronomy (2007 - present)

Crop Science Society of America (2007 - present)

Soil Science Society of America (2007 - present)

Gamma Sigma Delta (2008-present)

Phi Theta Kappa Society (2005-present)

Certificates

Completed a Certificate in Business Administration 

Spring 2011 program (http://www.business.illinois.edu/cib/) at the Univ. of Illinois 

Urbana-Champaign in which I strengthened my understanding and abilities in the 

functional areas of business.

References

Germán Bollero. Department Head and Professor of Biometry and Cropping Systems. 

Dept. of Crop Sciences. Univ. of Illinois. Mail: W-201 Turner Hall, 1102 S. Goodwin Ave, 

Urbana, IL 61801. E-mail: gbollero@illinois.edu. Phone: (217) 333-9475.

DoKyoung "D.K." Lee. Assistant Professor of Biomass and Bioenergy Crop Production. 

Dept. of Crop Sciences. Univ. of Illinois. Mail: S-320 Turner Hall, 1102 S. Goodwin Ave, 

Urbana, IL 61801. E-mail: leedk@illinois.edu. Phone: (217) 333-7736.

Thomas B. Voigt. Associate Professor and Extension Turfgrass Specialist, Dept. of Crop

Sciences. Univ. of Illinois. Mail S-416 Turner Hall, 1102 S. Goodwin Ave, Urbana, IL 

61801. E-mail: tvoigt@illinois.edu. Phone: (217) 333-7847.